2.2 Implementation of the Investigation

2.2.1 Organization of the Investigation

Starting from January 2022, we have conducted sufficient team discussions to determine the content and purpose of the survey: to study the understanding and satisfaction of different groups of people on microalgae water purification technology, and to initially design the content of the questionnaire. From February to April 2022, we conducted a two-month pre-survey. By analyzing the pre-survey data, we screened and replaced unqualified questions to improve the questionnaire. After confirming the formal questionnaire, we began to formally release the questionnaire to the public, and planned the time for returning the questionnaire, data analysis, report writing and other work arrangements.

2.2.2 Quality Control

(1) Questionnaire Quality

In the formal questionnaire given, we set up a set of jump questions, starting from "what is your current occupation?" and "do you know anything about microalgae?", This measure can effectively distinguish user categories and be more targeted.and different survey questions are given based on different user responses.

(2) Investigation Fee

Conducting research in the form of electronic questionnaires can reduce the printing and binding costs of paper questionnaires.

3 Pre-survey Data Verification

3.1 Reliability Test

Reliability refers to the reliability or consistency of the measurement results. The higher the reliability, the more consistent or stable the measurement results. This questionnaire adopts the analysis based on the Cronbach Alpha coefficient method for reliability analysis. When the Alpha coefficient is greater than 0.7, it indicates that the sample reliability is high. We divided the questionnaire into microalgae-related issues and waste water treatment-related issues and asked three types of users. The users were divided into different occupations: corporate incumbents, biological-related professionals, and the public. SPSS software was used for reliability test to check the consistency of the scale.x

Table 1 Reliability Test of Microalgae Related Issues

User occupation type	Cronbach Alpha based on normalization term	number of items	Evaluation of reliability
corporate employees	0.849	28	very high
Bio-related professionals	0.754	38	very high
the masses	0.725	22	very high

Table 2 Reliability Test of waste water Treatment Related Issues

User occupation type	Cronbach Alpha based on normalization term	number of items	Evaluation of reliability
corporate employees	0.718	17	very high
Bio-related professionals	0.794	2	very high
the masses	0.758	6	very high

It can be clearly seen from the table that the coefficients of the three aspects of microalgae-related issues and waste water treatment-related issues are all greater than 0.7, and the questionnaire has high reliability.

3.2 Validity Test

Validity refers to the degree to which measurement tools or means can accurately measure the things to be measured, and exploratory factor analysis is used to test the validity. There are two main steps in the validity test:

Step 1: Carry out the Bartlett sphere test to test whether the data can be used for factor analysis;

Step 2: Rotate the factor analysis to obtain the factor loading value of each option, and calculate the cumulative explained overall variance variation.

The Bartlett sphericity test is used to judge whether the correlation matrix is a unit matrix, that is, whether each variable has a strong correlation. If P<.05, the sphericity test is not obeyed, and the assumption that each weight variable is independent should be rejected, that is, there is a strong correlation between the variables; when P>.05, the spherical test is obeyed, and the variables are independent of each other, and factor analysis cannot be done.

Table 3 Microalgae Related Issues Bartlett Test

User occupation type	Bartlett test	Approximate chi-square
corporate employees	<0.01	193.301
Bio-related professionals	<0.01	868.249
the masses	<0.01	151.469

Table 4 Bartlett Inspection for waste water Treatment Related Issues

User occupation type	Bartlett test	Approximate chi-square
corporate employees	<0.01	45.304
Bio-related professionals	<0.01	93.302
the masses	<0.01	13.909

The above table is the result of the validity test, the P values are all <0.05, and there is a strong correlation between the variables. Therefore, the structural classification of the scale is reasonable.

3.3 Project Analysis Test

Item analysis can test the distinction of each item in the scale. Specifically, it is to test whether some of the surveyed subjects can give high scores in the scale, and some respondents can give high scores in the scale. It means that each item in the scale has better discrimination. The essence is to explore the differences of subjects with high and low scores in each item.

Take the question "Have you felt the benefits of the application of microalgae in your daily life?" as an example for project analysis. There are 5 options in the question, and the answer to each option ranges from "very impressed" to "neither listening nor feeling." were set to 1-4 points respectively, and those with a setting lower than 27% were classified as low, and those higher than 73% were classified as high.The item analysis test was carried out, and the results are shown in the table. The significance level of the high and low groups is P<0.05, indicating that the setting with the item has a high degree of discrimination and can distinguish the attitudes of different respondents. At the same time, the item analysis of other items has passed, that is, the questionnaire has a good degree of discrimination.

Table 4 Subjective Attitude

Item		P value-assuming equal variances
How receptive are you to the application of microalgae in the following areas?		0.00
Which of the following characteristics of microalgae technology are you most optimistic about?		0.00
What do you think is the main reason why the development of microalgal cell factories may be limited?		0.00
How well do you research the relevant functional mechanisms of microalgae?		0.00

3.4 Chi-square Test of Independence

The independence test can be used to judge whether two variables are related to each other or are independent of each other. Take the variables "Have you experienced the benefits of using microalgae in your daily life" and "How well do you know about fucoxanthin (one of the brown algae extracts) and its effects?" The chi-square independence test is performed, and the hypothesis test problem is:

H0: The two variables are irrelevant

H1: Two variables are correlated

Table 5 Chi-square Test of Independence

	Value	Degrees of freedom	Progressive significance (two-sided)
Pearson's chi-square	66.542	16	<0.001
Likelihood ratio	66.091	16	<0.001
Number of valid cases	86

The asymptotic significance is all less than 0.05, so the null hypothesis can be rejected, indicating a correlation between the two variables, that is, people who experience the benefits of microalgae in their lives know about fucoxanthin. Therefore, the chi-square independent test is performed on other variables in turn, and it can be found that there is a pairwise correlation between most of the variables.

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4 Data Processing and Analysis

4.1 Data Processing

4.1.1 Data Output

Through the questionnaire network platform, the user's detailed information can be exported to an excel file. In the exported data file, according to the order of options, it is coded in numerical order from small to large, indicating the content of the user's answer to each question.

4.1.2 Data Cleaning

(1) Eliminate the samples that did not complete the required questions, that is, the samples that did not complete the questionnaire;

(2) According to the user's filling and submitting time, the samples with too short or too much filling time are excluded.

4.2 Descriptive Statistics

4.2.1 Age Distribution of Sample People

Among the respondents surveyed, a total of six age groups were covered. The survey population is mainly concentrated between the ages of 18 and 25. At the same time, this group of people is also the main recipient and consumer of emerging things. Their attitudes and acceptance of genetically modified microalgae to develop new energy can to a certain extent reflect the development trend of the industry in the next few years.

4.2.2 Educational Distribution of Sample Personnel

Among the respondents, there are a total of 7 educational backgrounds, and the majority of them have a bachelor's degree or above. The specific proportion is as shown in the figure above. Among the surveyed population species, undergraduates and above account for the majority, and the population has more relevant knowledge and can give a more correct judgment on the new energy of microalgae.The data can well represent the attitudes of high-knowledge population towards the production of new energy by transgenic microalgae.

4.2.3 Occupational distribution of sample personnel

Table 6 Analysis of the Degree of Understanding of Microalgae by Different Occupations

The investigators included thirteen occupations, of which students accounted for the majority. In addition to freelancers in all occupations, the understanding rate of microalgae reached 88.9%, and the chance caused by the small sample cannot be ruled out. More than 30% of people in other occupations have no knowledge of microalgae before. The lack of understanding of microalgae in the whole society may affect the promotion of microalgae industry.

5. User Cognition Analysis of the Model based on Bloom's Cognition

5.1 Bloom's Cognitive Process Dimension

In Bloom's educational goal classification theory, people's cognitive process is divided into six dimensions: memory, understanding, application, analysis, evaluation, and creation, to represent the different stages of the understanding of things[2].

First, the memory dimension, refers to the recognition and memory. This level involves the identification of specific knowledge or abstract knowledge, in a form very close to an idea and phenomenon that students had first encountered.

Second, the dimension of understanding refers to the understanding of things, but it does not require a deep understanding, but it is preliminary, which may be superficial. It includes "transformation", interpretation, inference, etc.

Third, the application dimension refers to the application of the concepts, principles and principles learned. It requires learning to correctly apply abstract concepts to appropriate situations without explaining problem solving patterns. The application mentioned here is a preliminary direct application, rather than a comprehensive, through analysis, comprehensive use of knowledge.

Fourth, the analysis dimension refers to the decomposition of the material into its components, so that the mutual relationship between the concepts is more clear, the organization structure of the material is clearer, and clarify the basic theory and basic principles in detail.

Fifth, the dimension of evaluation. The requirement of this level is not to make a judgment based on intuitive feelings or observation phenomena, but to make a rational and profound persuasive judgment on the value of the essence of things. It integrates internal and external data and information to make inferences in line with objective facts.

Sixth, the creation dimension is based on the analysis, and recombine them into the whole again, so as to solve the problem comprehensively and creatively. It involves distinctive expressions, making reasonable plans and feasible steps, and launching certain rules according to basic materials. It emphasizes characteristics and initiative, and is a high-level requirement.

5.2 User Cognitive Model based on CRITIC

5.2.1 The Weights are Calculated by the CRITIC Method

In order to study which level of users' cognition of microalgae waste water treatment technology comes from, by analyzing the influence of the questions on the degree of product demand, the CRITIC weight method is used to measure the objective weight of the questions, considering the variability of the questions and the correlation between the questions[3]. If the standard deviation is used to indicate the differential fluctuation of the values within the option, the larger the standard deviation, the greater the numerical difference of the option, the more information can be shown, and the stronger the influence of the option itself. Using the correlation coefficient to represent the correlation between indicators, the stronger the correlation with other options, the less the option conflicts with other options, reflecting the more the same information, which somewhat weakens the influence of the option.

So, we first selected the questionnaire for cognitive dimensions, such as " Do you understand how microalgae helps achieve carbon neutrality?"" How do you know about the development of microalgae treatment technology?"et al., the higher the value of these options, the higher the understanding. The lower the value, the lower the understanding degree. The larger the standard deviation, the greater the numerical difference in the option, and the greater the impact of the option of choice on whether to know the algae product. The larger the correlation coefficient, the more information it reflects the same as the other option, and then the choice of the option has less impact on knowing the product. Therefore, consider the two factors comprehensively, calculate the information quantity, and get its weight. The specific steps are as follows:

Step 1: Forward processing of the data, dimensionless processing;

Step 4: Calculate the information quantity;

Step 5: Calculate the weights.

The weight calculation results are shown in Table 6:

Table 6 The Weight of the Six Major Dimensions of Cognition

Understanding dimension	memory	understand	apply	analyse	appraise	create
weight	0.0967	0.1127	0.4642	0.1189	0.1000	0.1076

5.2.2 Users’ Understanding Degree prediction

β:(i= 1,2..5)X1、X2、X3、X4、X5、X6 The regression equation is established by obtaining the weights of the six cognitive dimensions, where the coefficient is the weight of the five major requirements, and the variable represents the options selected by the user for the five types of problems. By calculating the Y value, it can be expressed as the user's acceptance score of the product.

The scores were calculated to indicate the user's understanding of the microalgae water purification technology. It was found that among the user data collected, 211 people had never heard of the microalgae, with a score of 0, which was defined as users to be popularized. Among the remaining users who know about microalgae, 24.17% of the predicted score is higher than 0.8,2.60% is below 0.3, and the rest is the middle score, which shows that our prediction score is in line with the general phenomenon.

6. Potential User Mining Model based on K-Clustering

6.1 Model Background

In modeling for the questionnaire results found that different users of microalgae water purification technology, how to through further analysis, understanding of microalgae water purification technology of the corresponding characteristics of different crowd, help to further analyze the corresponding science popularization means and the scope of propaganda, for microalgae water purification technology promotion and improve the corresponding recognition for reference.

6.2 User Mining Model Building

6.2.1 Cluster Analysis based on AESPU

For the cluster analysis of the questions for potential users in the questionnaire, the clustering factors are preferred[4]. Starting from the characteristics of users and the previous analysis of user cognitive model, we set five cluster analysis indicators: A, E, S, P and U.

Table 8 Potential User Task Meaning

The collected user data were analyzed, and the relationship between k and SSE and the profile coefficients were calculated, dividing the users into 5 categories for cluster analysis by python.

metric	A	E	S	P	U
meaning	age	sex	record of formal schooling	occupation	Understand the degree

6.2.2 Potential User Composition

User data were clustered, and the clustering results are shown in Table 8,

The Table 9 Clustering Results

	Z_ age	Z_ sex	Z_ record of formal schooling	Z_ occupation	Z_ score	Number of categories
0	0. 739849	-0. 262072	0. 137494	-0. 391089	0. 048058	190
1	-0. 335075	-0. 354205	0. 019592	2. 693135	0. 027030	34
2	-0. 275357	2. 706168	0. 313416	0. 396105	0. 078421	26
3	-1. 290563	-0. 311768	0. 153617	-0. 372527	-0. 067940	102
4	0. 739849	1. 790504	-3. 892901	0. 803027	-0. 083163	13

Draw the radar map of various users and analyze the attributes of five types of users. From the figure, it can be found that each user group has significantly different performance characteristics: user group 1 is the largest in age and understanding attributes, and the smallest in career attributes; user group 3 is relatively small in education and understanding attributes; user group 4 is evenly distributed in various attributes; user group 5 is the largest in gender attributes, and the distribution of other attributes is evenly distributed.

Based on the feature description, this paper defines four levels of user categories: important potential users, important development users, general users, and low-value potential users. The characteristics of each customer type are as follows:

(1) Important potential users: mainly for the first group of people, the biggest characteristic is higher education, older age, the analysis may be college teachers or highly educated elderly group, rich experience, pay more attention to and understand the microwater purification of algae and other related knowledge.

(2) Important development users: mainly in the third group, who are highly educated but young, and most of them are college students receiving higher education. Such users are in the new development force of the society and have a high acceptance degree of new things. According to the characteristics, this population's awareness of microalgae water purification technology needs to be improved, and it may develop into important potential users in the future.

(3) General users: This kind of user group is relatively complex, consisting of the second cluster of people. According to the user characteristics, they are mainly female groups with low educational background, and have a certain understanding of microalgae water purification technology.

(4) Low-value potential users: that is, the fourth and fifth clusters, except for the fifth cluster, are mostly female, and the special characteristics of these two types of users are relatively stable, with no features more significantly different from other users.

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7. Conclusion and strategy

7.1 Research conclusions

Through the user data obtained through the online questionnaire, this paper deeply studies the users' understanding of the microalgae water purification technology, starting from the facts, cognition, composition and emotional components. The views and conclusions of this paper are as follows:

Users are not well aware of microalgae and microalgae water purification technology. The current non-professional technical personnel of microalgae and its water purification related principles technology is weak, mainly more stay in memory level understanding, is simply heard of the corresponding products, no specific understanding of microalgae application in various fields, as well as the corresponding water purification measures and policies, more there is no existence of microalgae water purification have corresponding understanding and improvement Suggestions.

In mining potential users, enterprises need to focus on the development of highly educated or have rich experience in the elderly, such users for microalgae water purification technology crucial group, at the same time, will accept higher education of college students into the next development of customers, by enhancing propaganda, drive this group for micro algae water purification technology understanding, thus to join this kind of high knowledge crowd to further promote the development of industry.

7.2 Suggestions

7.2.1Expand product publicity channels

(1) Strengthen the science popularization and publicity work

User understanding of microalgae and its water purification technology is largely from the microalgae products, and the lack of cognition of microalgae transgenic technology is closely related, enterprises should pay attention to transgenic microalgae products science propaganda work, through the system of information dissemination channels, keep the mass media: such as television, newspaper propaganda, strengthen new media platform such as: WeChat public, weibo position propaganda, effectively regulate and control pseudoscience information dissemination, ensure strong credibility and authority of product propaganda.

(2) Pay attention to key groups

In the face of different understanding degrees and contributions of different groups to microalgae and water purification related information, microalgae enterprises should focus on the cognitive differences of different groups, form a situation of information dissemination from point to surface, and improve the efficiency of publicity.

7.2.2 To ensure product safety and improve laws and regulations

(1) Improve the detection technology of microalgae products

Testing of transgenic microalgae products is necessary to ensure their quality and safety. With the continuous development of gm food, countries and related international organizations are actively carrying out transgenic product testing technology, at present, the transgenic products of the most mature technology is P CR technology, enterprises should be on the basis of international and domestic technology, looking for more accurate, fast, safe detection technology, strengthen the safety assessment of gm products, let users can safely use transgenic microalgae products.

(2) Improve relevant laws and regulations

To promote transgenic microalgae products, must strengthen the improvement of relevant laws and regulations, strictly control the use of transgenic products scientific field and species, not only to make consumers' right to know, choice and other legitimate rights and interests get reimbursement, more attention to control the microalgae harm to the ecological environment, it is strictly prohibited for enterprises or factories to influence and harm the ecological environment system operation[5].

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References:

• [1] Zeng Wuyi,Huang Bingyi.Analysis of the credibility and validity of the questionnaire[J]. Statistics and Information Forum, 2005(06):13-17.

• [2] Farzad Radmehr,Michael Drake. Revised Bloom’s taxonomy and major theories and frameworks that influence the teaching, learning, and assessment of mathematics: a comparison[J]. International Journal of Mathematical Education in Science and Technology,2019,50(6).

• [3] Wang Siqi,Wei Guiwu,Lu Jianping,Wu Jiang,Wei Cun,Chen Xudong. GRP and CRITIC method for probabilistic uncertain linguistic MAGDM and its application to site selection of hospital constructions[J]. Soft Computing,2021,26(1).

• [4] Ali Ishfaq,Rehman Atiq Ur,Khan Dost Muhammad,Khan Zardad,Shafiq Muhammad,Choi JinGhoo. Model Selection Using K-Means Clustering Algorithm for the Symmetrical Segmentation of Remote Sensing Datasets[J]. Symmetry,2022,14(6).

• [5] Lv Ping. How to use and promote genetically modified food scientifically and reasonably——Analysis of questionnaires based on consumer attitude towards genetically modified food[J]. Market Modernization, 2015(07):30.

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Appendix

A questionnaire submitted to the relevant personnel of the enterprise

Measuring dimensions	Measuring questions
Fact component	【Single choice】Does your industry fall into which of the following categories?
	【Multiple choice】Which of the following methods has been used in your wastewater treatment?
	【Single choice】Does your company have a special waste water discharge supervision institution?
Cognitive component	【Multiple choice】In response to the sustainable development strategy, the country's investment in environmental governance has increased significantly, and the comprehensive water pollution control of key rivers and waters has been strengthened. Which of the following policies do you know about the supervision of waste water discharge?
	【Multiple choice】Which of the following wastewater treatment measures do you know?
	【Multiple choice】Which of the following waste water discharge supervision content do you know?
	【Scale questions】The government work report of the State Council in 2021 pointed out that we should do a good job in carbon peaking and carbon neutralization, and formulate an action plan for carbon emission peaking before 2030, in which microalgae also play an important role.How well do you understand the role of microalgae in the ecological environment?
	【Multiple choice】What difficulties do you think may exist in the development of microalgae factories?
	【Scale questions】The goal of synthetic biology is to build artificial biological systems that behave like electrical circuits. How well do you know about synthetic biology?
	【Scale questions】How much do you know about the principles of microalgae treatment of waste water?
	【Scale questions】How well do you understand the two principles of corporate waste water: the treatment principle of corporate domestic waste water and the principle of combining compliance with risk control?
	【Scale questions】On November 10, 2016, the State Council issued the "Implementation Plan for the Control of Pollutant Discharge Permit System", which attracted widespread attention, and keywords such as "reducing the burden on enterprises" officially entered people's attention. How well do you understand the cost of microalgae technology for wastewater treatment?
	【Scale questions】How do you know the process of microalgae treatment of waste water technology?
	【Multiple choice】Which of the following cutting-edge technological developments in the field of microalgae are you aware of?
Emotional component	【Multiple choice】What do you think are the disadvantages of traditional environmental detection methods?
	【Scale questions】In 2002, the 16th National Congress of the Communist Party of China took "continuous enhancement of sustainable development capability" as one of the goals of building a well-off society in an all-round way. Do you have high expectations for the large-scale development of microalgae for water pollution control in the future?
	【Multiple choice】If there is an opportunity to promote and use microalgae to treat waste water in the future, which of the following characteristics do you most hope this technology has?
	【Multiple choice】How would you most like to learn about microalgae?
	【Scale questions】Sustainable development not only meets the needs of the present while protecting the environment, but also looks forward to not harming the needs of future generations. Based on the above advantages, what is your acceptance of microalgae applications in energy, agriculture, food, medical, and industrial fields?
	【Scale questions】The Standing Committee of the National People's Congress implements the decisions and deployments of the CPC Central Committee and conducts inspections on the enforcement of the Marine Environmental Protection Law. Do you usually pay attention to the latest developments in the field of waste water treatment technology?
	【Multiple choice】What paths do you think your company mainly relies on in its efforts to achieve carbon neutrality?
Normal mode data analysis	【Single choice】Your age？
	【Single choice】Your gender？
	【Single choice】Your occupation？
	【Single choice】Your degree？
	【Fill in the blank】Your resident city？

Appendix II: The Public-Facing Questionnaire

dimension	Measuring questions
Fact ingredients	[Single choice] Is there any random discharge of waste water around you?
	[Scale questions] Do you feel the benefits of the application of microalgae in your daily life?
cognitive component	[Single choice] Have you ever heard of microalgae (small algae) before?
	[Scale questions] The purpose of synthetic biology is to build artificial biological systems so that they can run like circuits. How much do you know about synthetic biology?
	[Single choice] Do you understand how microalgae can help achieve carbon neutrality?
	[Single choice] Have you ever heard about the concept of "green carbon and blue carbon" in carbon neutral?
	【[Multiple choice] Which of the following organisms undertake the mission of "blue carbon"?
	[Multiple choice] At one point have you heard of the following cutting-edge technologies and developments in the field of microalgae?
	[Single choice] Do you usually pay attention to the latest technological development in the field of waste water treatment?
	[Scale] How do you know the development status of microalgae treatment technology?
Emotional components	[Multiple choice] What are some of the most important applications do you want to know about microalgae?
	[Scale question] Do you think that water pollution prevention is a key step in the sustainable development strategy?
	[Scale title] Microalgae can be used in the following aspects in daily life: biodiesel, agricultural algae fertilizer, antioxidants, waste water treatment, how do you expect the development of the above applications?
	[Duoxian] If you promote and use microalgae waste water treatment technology, what do you want the following characteristics of this technology?
	[Multiple choice] You think the application of synthetic biology technology can be implemented
	[Multiple choice] What path do you think you should rely on to achieve "carbon neutrality"?
	[Scale question] How do you expect the cost of microalgae waste water treatment technology?
	[Multiple choice] What are you most interested in microalgae controlling water pollution?
Normal mode data analysis	【Single choice】Your age？
	【Single choice】Your gender？
	【Single choice】Your occupation？
	【Single choice】Your degree？
	【Fill in the blank】Your resident city？

Appendix III is a questionnaire for the technicians

dimension	Measuring questions
Fact ingredients	[Scale question] How are you satisfied with the current water bodies (rivers, lakes, etc.)?
	[Multiple choice] In which field do you daily contact or understand microalgae?
	[Multiple choice] What path do you think your work unit mainly relies on for its efforts to achieve "carbon neutrality"?
cognitive component	[Scale questions] Do you have a thorough study of the related functions of microalgae?
	[Multiple choice] What do you think are the disadvantages of the traditional waste water treatment methods?
	[Multiple choice] What do you think is the main limitation in the development of microalgae cell factories?
	[Scale question] How much do you know about the microalgae-bacterial granular sludge process?
	[Single choice] Have you ever heard about the concept of "green carbon and blue carbon" in carbon neutral?
	[Scale question] How do you understand the policies and systems related to enterprise waste water treatment?
	[Scale question] How do you know algae flavin (one of brown algae extract) and its role
	[Scale question] Do you know the mechanism of microalgae biofilm on algae repair in polluted water?
	[Multiple topics] Establishing a green, low-carbon and circular development economic system is an important part of the building of a modern economic system. What do you have any understanding of the following cutting-edge technologies and development in the field of microalgae?
Emotional components	[Single choice] Given the sustainable development strategy, the national investment in environmental treatment has increased significantly, and the comprehensive water pollution treatment of key rivers and waters has been strengthened. Do you have high expectations that microalgae will be large-scale development and applied to water pollution treatment in the future?
	[Multiple choice] If you have the opportunity to promote and use microalgae to treat waste water in the future, what are the following characteristics of microalgae technology?
	[Scale title] Sustainable development meets the needs of contemporary people to protect the environment, but also without damages the needs of future generations, based on the above advantages, your acceptance of microalgae applied to the following areas
	[Multiple choice] Which of the following ways do you most like to promote the efficacy of microalgae?
	[Multiple choice] Which of the following things do you think microalgae control water pollution is most worthy of promotion?
	[Fill in the blanks] What do you think is the current microalgae technology?
	[Scale questions] How interested are you in studying microalgae?
Normal mode data analysis	【Single choice】Your age？
	【Single choice】Your gender？
	【Single choice】Your occupation？
	【Single choice】Your degree？
	【Fill in the blank】Your resident city？

          
 Appendix IV critic The matlab code for the calculation of the weights 
          
clc,clear
          
data =xlsread('data1.xlsx','Sheet1','G1:L576');
          
 From the data (:, 3) = to-data (:, 3); the% cost-type 
          
 index is converted to the benefit type 
          
[m, n] = size(data);
          
for i = 1:n
          
    data(:, i) = (data(:, i) - min(data(:, i)))/(max(data(:, i)) - min(data(:, i)));
            end
          
 The corr = corrcoef (data);% calculates the 
          
 correlation coefficient matrix 
          
 The corr_1 = sum (1-corr);% calculate conflict 
          
 data_std = std (data);% calculates variance per 
          
 column 
          
 C = data_std .* The corr_1;% calculates the 
          
 information quantity 
          
 w = C./ sum (C);% calculation weights 
          
 w 
          
 Appendix V The python code for the k-means clustering 
          
 import numpy as np 
          
import pandas as pd
          
from sklearn.cluster import KMeans
          
from sklearn import metrics
          
import matplotlib.pyplot as plt
          
from sklearn.datasets import make_blobs
          
 # Data preparation C://Users 
          
 data = 
          
 pd.read_excel('C://Users//15722//Desktop//data.xls 
          
 x') 
          
 columns = ["year", "gender", 
          
 "degree","profession",'score'] 
          
 data = np.array(data[0:])[:,:] 
          
 data = (data - data.mean(axis = 0))/(data.std(axis = 
          
 0)) #Concise statements implement standardized 
          
 transformations, similarly any desired 
          
 transformation 
          
 columns=['Z_'+i for i in columns] 
          
 #Sets the number of clusters 
          
 n_clusters = 5 
          
 # Build clustering model objects 
          
 kmodel= KMeans(n_clusters=n_clusters, 
          
 random_state=2018) 
          
 # Train a clustering model 
          
 kmodel.fit(data) 
          
 r1 = pd.Series(kmodel.labels_).value_counts() 
          
 #Counts the number of categories 
          
 r2 = pd.DataFrame(kmodel.cluster_centers_) 
          
 #Locate the cluster center 
          
 max = r2.values.max() 
          
 min = r2.values.min() 
          
 r = pd.concat([r2, r1], axis = 1) 
          
 r.columns = list(columns) + [u'cluster'] #Rename 
          
 the table header 
          
 print(r) 
          
 #r = pd.concat([data, pd.Series(kmodel.labels_, 
          
 index = data.index)], axis = 1) #Detailed output of 
          
 the corresponding category for each sample 
          
 #r.columns = list(data.columns) + [u'cluster'] 
          
 #Rename the table header 
          
 plt.rcParams['font.sans-serif'] = 'SimHei' 
          
 plt.rcParams['font.size'] = 12.0 
          
 plt.rcParams['axes.unicode_minus'] = False 
          
 plt.style.use('ggplot') 
          
 ggplot')# Drawing 
          
 fig = plt.figure(figsize=(10, 8)) 
          
 ax = fig.add_subplot(111, polar=True) 
          
 center_num = r.values 

          
feature = ["年龄", "性别", "学历","职业",'了解程度']
          
N = len(feature)
          
for i, v in enumerate(center_num):
          
   angles = np.linspace(0, 2 * np.pi, N, endpoint=False)
          
    center = np.concatenate((v[:-1], [v[0]]))
          
    angles = np.concatenate((angles, [angles[0]]))
          
    ax.plot(angles, center, 'o-', linewidth=2, label="第%d簇人群,%d人" % (i + 1, v[-1]))
          
    ax.fill(angles, center, alpha=0.25)
          
    ang = angles * 180/np.pi
          
   ax.set_thetagrids(ang[:-1], feature, fontsize=15)
          
    ax.set_ylim(min - 0.1, max + 0.1)
          
    plt.title('用户群特征分析图', fontsize=20)
          
ax.grid(True)
          
 plt.legend(loc='upper right', bbox_to_anchor=(1.3, 1.0), ncol=1, fancybox=True, 
          
shadow=True)
          
plt.show()
          
import matplotlib.pyplot as plt
          
from sklearn.cluster import KMeansfrom 
          
from sklearn import datasets, metrics
          
def km_sse_cs():
          
   """
          
   KMeans KMeans algorithm effect evaluation 
          
    1、Intra-cluster error variance, elbow method, elbow method, the size of which indicates how
            well the function fits. 
          
 Using the graphical tool elbow method, visualize the error variance within a cluster based on the number
            of clusters. When the rate of decline suddenly slows down, it is considered the optimal k-value (inflection
            point). 
          
    When the KMeans algorithm is trained, the error variance within the cluster can be obtained
            by using the built-in ininrtia property. 


          
    2、The profile coefficient method combines the cohesion and separation of clusters 
          
（Separation）
          
    The average profile coefficient ranges from [-1,1], and the larger the coefficient, the
            better the clustering effect. When the value is negative, it implies that the point may have been
            misdivided. 
          
   :return:
          
   """
          
    #sample = load_data()
          
   #data = sample.data
          
   #Stores the SSE values when setting different numbers of clusters 
          
    sse_list = []
          
     # Profile coefficient 
          
   silhouettes = []
          
   # silhouettes = [] 
          
    # Loops set a different number of clusters 
          
       for i in range(2, 15): 
          
       model.fit(data)
          
       model = KMeans(n_clusters=i) 
          
        model.fit(data) 
          
       # The kmeans algorithm inrtia property gets the SSE within the cluster 
          
       sse_list.append(model.inertia_ 
          
        # Profile coefficient 

          
        silhouette = metrics.silhouette_score(data, 
          
    #model.labels_, metric='euclidean') 
          
         silhouettes.append(silhouette) 

          
     # Plots the error variance curve within the cluster 
          
     plt.subplot(211) 
          
     plt.title('KMeans Error variance in the cluster') 
          
     plt.plot(range(2, 15), sse_list, marker='*') 
          
     plt.xlabel('The number of clusters') 
          
     plt.ylabel('SSE') 
          
     # Draw a profile coefficient curve 
          
     plt.subplot(212) 
          
     plt.title('KMeans Silhouette Coefficient') 
          
     plt.plot(range(2, 15), silhouettes, marker='o') 
          
     plt.xlabel('The number of clusters') 
          
     plt.ylabel('Silhouette Coefficient') 
          
     plt.tight_layout() 
          
     plt.show()

          
     #The KMeans algorithm 

          
    km_sse_cs