# Prediction of College Students’ Psychological Crisis Based on Data Mining

Authors: Jingjing Liu; Guangyuan Shi; Jing Zhou; Qiumei Yao
Journal: Mobile Information Systems
 (2021)

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## Abstract

The development of a college students’ psychological management system has become an essential indicator to monitor and prevent the psychological crisis. University student management databases accumulate massive data, but the conventional data processing tasks are restricted to simple statistical analysis, storage, and query management. This paper discusses the application of big data technology for the current psychological management system by investigating psychological crisis screening indicators. Data mining techniques are used to realize the dynamic management of psychological early warning data, real-time monitoring of high-risk groups’ psychology, and improvement of the accuracy and effectiveness of early identification and warning of students’ psychological crisis. Based on a combination of qualitative and quantitative analysis, we conduct a series of studies on three typical types of network public opinions, i.e., Internet rumors, online public opinions of college students, and emergent public health incidents in terms of the transmission mechanism, early warning, and decision-making mechanism, as well as the evolution mechanism of the network public opinion.


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## Body

## 1. Introduction

College students are a representative group of netizens. They are highly educated and have a much higher utilization rate of the Internet. They are a powerful force in the network public opinion and have a significant influence within various groups. The way students are organized determines the nature of problems and confusions that exist and are often revealed and disseminated through the Internet. Compared with the general public’s online opinion, college students’ opinion contains more content and spreads faster. Therefore, it is of significant interest to study specific network public opinion judgment by taking college students as the representatives. Considering the characteristics of college students’ online public opinion, using intuitionistic fuzzy reasoning judgment, analytic hierarchy process, and expert scoring method, a set of network public opinion theory model is constructed to provide theoretical basis and data support for college students’ online public opinion management.In recent years, college students’ mental health has aroused the general concern of the whole society. The in-depth study of college student’s mental health and the exploration of college students’ psychological intervention mode are the hotspots of scholars worldwide. Data mining technology is incomparable to other technologies in mining hidden rules in business data and solving specific problems. With the maturity of data mining technology, many scholars continue to research methods and apply data mining techniques to universities’ teaching and management fields. The authors of this paper studied sufficient literature by conducting in-depth research and study on data mining technology and tried to apply it to the data of college students’ psychological problems. This paper discusses the basic principles and concepts of data mining technology. The tasks, methods, and application fields of data mining are discussed to analyze its feasibility for solving college students’ psychological problems by making a detailed analysis of fuzzy mathematics. This paper uses a decision tree algorithm to calculate which attribute is most relevant to psychological issues (i.e., the attribute with the most significant information gain value) as the decision tree’s root node. Next, it uses an iterative, recursive method to classify the remaining attributes with the same algorithm to form a decision tree. It establishes a classification tree model to predict and analyze the psychological problems of college students. The Apriori algorithm is used to analyze the internal relationship between students’ psychological problems and students’ attributes to provide decision-making support information for the school psychological counseling center and make mental health education more targeted and purposeful. The author independently developed an excellent performance, simple, and practical data mining system for psychological problems. The system has a friendly interface and has data file access, data preprocessing, decision tree generation, association rules generation, rule interpretation, and rule preservation. With this data mining system, some knowledge of classification rules and association rules is obtained successfully. Considering students’ psychological problems in our university as an example, the function verification experiment of data mining of students’ psychological problems is carried out. Finally, it points out the shortcomings of this paper and the direction of future efforts.The rest of this paper is organized as follows. In Section2, the related work is discussed. In Section 3, the intuitionistic fuzzy early warning model of college students’ opinion is presented. In Section 4, a dynamic method based on bootstrap estimation is proposed for prediction of college students’ psychological crisis based on data mining. In Section 5, the experimental results and analysis are discussed. Finally, the paper is concluded with future research directions in Section 6.


## 2. Related Work

Fuzzy mathematics is a branch of mathematics that studies and deals with ambiguity in mathematical problems. The basis of modern mathematics is Cantor’s set theory. This theory defines that each set consists of explicit elements that either belong to or do not belong to a certain set and cannot be ambiguous. However, many words in the objective world have no clear quantitative boundaries, such as youth, tallness, and heat. The phenomena that exist in these objective worlds are also called ambiguity. Probability has been used in traditional uncertainty studies. The classical inductive probability logic assumes that the parties can exhaust all experiments’ possible outcomes and know the probability of any event outcome. They cannot express ignorance, discord, distrust, and lack of trust. Zadeh believes that elements only belong to or do not belong to a specific set in Cantor’s set theory [1]. Such binary logic has little place. In 1965, fuzzy set theory was proposed, which created fuzzy mathematics and applied mathematical methods to study ambiguity. In fuzzy set theory, for any element u∈U in a given universe U, a corresponding real number uAu∈0,1 belonging to the interval [0, 1] is given. This corresponding number is called the membership of u to A and is used to describe the extent to which u belongs to A so that precise mathematical tools can be used to describe inaccurate ambiguities.Fuzzy evaluation deals with fuzzy evaluation objects through precise mathematical means and can make scientific, reasonable, and close-to-the-actual quantitative assessment of the data with vague information. The evaluation result is a vector, not a point value, and the information contained is rich. It can accurately describe the object to be evaluated and further process and obtain reference information. However, the fuzzy comprehensive evaluation method is computationally complex, and the subjectivity of the index weight vector is strong. When the index set U is large, i.e., the number of index sets is large, under the conditional constraint that the weight vector is 1 (the relative membership weight), then the coefficients tend to be too small, and the weight vector does not match the fuzzy matrix. Moreover, the result is superfuzzy, and the resolution is very poor. It is impossible to distinguish who has a higher degree of membership or even can cause the judgment to fail. Dempster-Shafer [2, 3] proposed a trust and likelihood function to apply it to uncertainty measurement. It generalizes the classical probability and proposes a method to describe the parties’ insufficient ignorance in determining the probability. It has been widely studied and applied in different fields. However, fuzzy set theory only contains the membership degree of the element, that is, it can only be used to describe the evidence of support and unsupported elements, while ignoring that the element may not support or oppose, that is, the nature of neutrality.To solve this problem better, Atanassov [4] first proposed an intuitionistic fuzzy concept in 1986. He proved that the fuzzy set is only a special case of the intuitionistic fuzzy set. The intuitionistic fuzzy set uses the nonmembership function to represent the neutral state of the element for the set, which can more accurately describe the object’s ambiguity. Intuitionistic fuzzy sets have better inference accuracy and description ability than fuzzy sets. Intuitionistic fuzzy set theory has been developed and applied to various problems. Several scholars have performed significant studies [5–9] on this theory by presenting the algorithms of intuitionistic fuzzy sets. By combining intuitionistic fuzzy sets with L-fuzzy sets and interval-valued fuzzy sets, straight L-fuzzy sets and interval values were proposed. For this purpose, the author in [10] first proposed two theorems about modular operators but did not explicitly point out these theorems’ meaning. Until 2003, Atanassov [11] revisited these two theorems in the literature, used them to discuss the separation conditions of intuitionistic fuzzy sets, and finally applied them to the decision-making process. Other scholars have performed a lot of research on the theory of intuitionistic fuzzy sets. Hu et al. [12] defined the concepts of expansion and concentration of intuitionistic fuzzy sets and discussed their properties. Various examples show that these concepts are very useful for dealing with linguistic variables. Bustince and Burillo [13–16] proved that the vague set is an intuitionistic fuzzy set and studied some functional properties of the intuitionistic fuzzy relation and the entropy of the S-intuitionistic fuzzy set. The authors in [17]–19] proposed a very close relationship between D-S theory and fuzzy set theory and offered a new likelihood function intuitionistic fuzzy set reasoning. At present, people’s research on fuzzy reasoning continues to deepen and promote the development of theory.


## 3. Intuitionistic Fuzzy Early Warning Model of School Students’ Internet Public Opinion

In this paper, the research on the network public opinion of colleges and universities is carried out. The analysis mode of college students’ online public opinion is used to express the public opinion situation. The purpose is to explain the evolution law of college students’ network public opinion from a deeper level. We use the intuitionistic fuzzy set theory and the analytic hierarchy process to construct a mathematical model to monitor the college students’ network public opinion and determine the network public opinion warning level.


### 3.1. Intuitionistic Fuzzy Set of College Students’ Online Public Opinion Warning Level

We adopt a linear mapping method for mapping the regional distribution of audience, the number of people participating in the discussion, the speed of the network sensation of the university, the source of the network grievances of the university, the way of its dissemination, the public’s emotional tendency, attention, and hotspots to the values in the interval [0, 1].For calculating the membership degree of each factor, the intuitionistic fuzzy comprehensive evaluation method is used. The factors involved in the reasoning include the analysis of the topic itself, the audience’s analysis, and the analysis of the connection between the audience and the topic. Correspondingly, their respective domains are the domain of importance of a particular topic, the audience’s reflection domain, and the domain of contact between the audience and the topic.


#### 3.1.1. Judging the Network Public Opinion Warning Level

The warning level is determined using the following factors.(1)
Intuitionistic fuzzy subsets reflecting the importance of a particular topicThe importance of a particular topic can be derived from the method described above for calculating the importance of the topic. LetX1∈0,1 be the normalized importance, and record the corresponding domain A = [0, 1]. The membership functions constructing the intuitionistic fuzzy subsets A1=general and A2=general and their intuition indices are each(1)uA1=0.9exp−x122δu2,uA2=0.9exp−1−x122δu2,πA1=0.1exp−0.5−x122δπ2,πA1x1=0.Letδu=0.1416 and δπ=0.035, then the intuitionistic fuzzy set subsets constructed under the domain W can be written separately as x1,uA1,γA1andx1,uA2,γA2.(2)
Intuitive fuzzy subsets representing audience responsesAudience response can be obtained through the combination of the number of audiences participating in the discussion and the speed of the spread of college students’ online public opinion. The membership function of the intuitionistic fuzzy set representing the audience response can be calculated by weighted average using intuitionistic fuzzy comprehensive evaluation. LetX2∈0,1 be the number of normalized audiences, X3∈0,1 be the speed of normalization, the domain that the audience reflects B=0,1×0,1, and the intuitionistic fuzzy subsets under the domain are B1=slow and B2=fast. The subset of intuitionistic fuzzy sets that describe the audience response can be written as x2,x3,uB1,γB1andx2,x3,uB2,γB2. Construct the membership function and the intuitionistic index of the intuitionistic fuzzy subsets, respectively:(2)uB1x2,x3=0.45exp−x222δu2+exp−x322δu2,uB2x2,x3=0.45exp−1−x222δu2+exp−1−x322δu2,πB1x2,x3=0.05exp−0.5−x222δu2+exp−0.5−x322δu2,πB1x2,x3=0.(3)
An intuitionistic fuzzy subset describing the interrelationship between the audience and the topicAnalyze the sentimental tendency of the public in expressing speech, the public’s attention to the college’s online public opinion, and whether the topic is a hot topic, using the weighted average operation in the intuitionistic fuzzy comprehensive evaluation to construct a description of the relationship between the audience and the topic. For the intuitionistic fuzzy subset membership of the relationship, letX4∈0,1 be the normalized attitude bias, X5∈0,1 be the normalized attention, X6∈0,1 be the normalized increase in concern, and X7∈0,1 be the normalized duration of concern. The corresponding intuitionistic fuzzy subset C1=sparseandC2=tight; the intuitionistic fuzzy subset describing the interrelationship between the audience and the topic is x4,x5,x6,x7,uC1,γC1andx4,x5,x6,x7,uC2,γC2. Construct the membership function and the intuitionistic index of two intuitionistic fuzzy subsets, respectively:(3)uC1x4,x5,x6,x7=0.225exp−x422δu2+exp−x522δu2+exp−x622δu2+exp−x722δu2,uC2x4,x5,x6,x7=0.225exp−1−x422δu2+exp−1−x522δu2+exp−1−x622δu2+exp−1−x722δu2,πC1x4,x5,x6,x7=0.225exp−0.5−x422δπ2+exp−0.5−x522δπ2+exp−0.5−x622δπ2+exp−0.5−x722δπ2,πC2x4,x5,x6,x7=0.(4)
Early warning level of college students’ online public opinionAccording to the four types of college students’ online public opinion, the level of urgency is different. The intuitionistic fuzzy subsets under the early warning domain areZ1 = {levelIV}, Z2 = {level III}, Z3 = {levelII}, and Z4 = {levelI}. Constructing the membership function and intuitionistic fuzzy index of each of the four subsets as follows:(4)uZ1z=0.9exp−z22δu2,uZ2z=0.9exp−1/3−z22δu2,uZ3z=0.9exp−2/3−z22δu2,uZ4z=0.9exp−1−z22δu2,uZ4z=0.9exp−1−z22σu2,πZ1z=0.1exp−1/6−z22σπ2,πZ2z=0.1exp−1/2−z22σπ2,πZ3z=0.1exp−5/6−z22σπ2,πZ4=0,where z∈0,1,σu=0.07,andσπ=0.035.


## 3.1. Intuitionistic Fuzzy Set of College Students’ Online Public Opinion Warning Level

We adopt a linear mapping method for mapping the regional distribution of audience, the number of people participating in the discussion, the speed of the network sensation of the university, the source of the network grievances of the university, the way of its dissemination, the public’s emotional tendency, attention, and hotspots to the values in the interval [0, 1].For calculating the membership degree of each factor, the intuitionistic fuzzy comprehensive evaluation method is used. The factors involved in the reasoning include the analysis of the topic itself, the audience’s analysis, and the analysis of the connection between the audience and the topic. Correspondingly, their respective domains are the domain of importance of a particular topic, the audience’s reflection domain, and the domain of contact between the audience and the topic.


### 3.1.1. Judging the Network Public Opinion Warning Level

The warning level is determined using the following factors.(1)
Intuitionistic fuzzy subsets reflecting the importance of a particular topicThe importance of a particular topic can be derived from the method described above for calculating the importance of the topic. LetX1∈0,1 be the normalized importance, and record the corresponding domain A = [0, 1]. The membership functions constructing the intuitionistic fuzzy subsets A1=general and A2=general and their intuition indices are each(1)uA1=0.9exp−x122δu2,uA2=0.9exp−1−x122δu2,πA1=0.1exp−0.5−x122δπ2,πA1x1=0.Letδu=0.1416 and δπ=0.035, then the intuitionistic fuzzy set subsets constructed under the domain W can be written separately as x1,uA1,γA1andx1,uA2,γA2.(2)
Intuitive fuzzy subsets representing audience responsesAudience response can be obtained through the combination of the number of audiences participating in the discussion and the speed of the spread of college students’ online public opinion. The membership function of the intuitionistic fuzzy set representing the audience response can be calculated by weighted average using intuitionistic fuzzy comprehensive evaluation. LetX2∈0,1 be the number of normalized audiences, X3∈0,1 be the speed of normalization, the domain that the audience reflects B=0,1×0,1, and the intuitionistic fuzzy subsets under the domain are B1=slow and B2=fast. The subset of intuitionistic fuzzy sets that describe the audience response can be written as x2,x3,uB1,γB1andx2,x3,uB2,γB2. Construct the membership function and the intuitionistic index of the intuitionistic fuzzy subsets, respectively:(2)uB1x2,x3=0.45exp−x222δu2+exp−x322δu2,uB2x2,x3=0.45exp−1−x222δu2+exp−1−x322δu2,πB1x2,x3=0.05exp−0.5−x222δu2+exp−0.5−x322δu2,πB1x2,x3=0.(3)
An intuitionistic fuzzy subset describing the interrelationship between the audience and the topicAnalyze the sentimental tendency of the public in expressing speech, the public’s attention to the college’s online public opinion, and whether the topic is a hot topic, using the weighted average operation in the intuitionistic fuzzy comprehensive evaluation to construct a description of the relationship between the audience and the topic. For the intuitionistic fuzzy subset membership of the relationship, letX4∈0,1 be the normalized attitude bias, X5∈0,1 be the normalized attention, X6∈0,1 be the normalized increase in concern, and X7∈0,1 be the normalized duration of concern. The corresponding intuitionistic fuzzy subset C1=sparseandC2=tight; the intuitionistic fuzzy subset describing the interrelationship between the audience and the topic is x4,x5,x6,x7,uC1,γC1andx4,x5,x6,x7,uC2,γC2. Construct the membership function and the intuitionistic index of two intuitionistic fuzzy subsets, respectively:(3)uC1x4,x5,x6,x7=0.225exp−x422δu2+exp−x522δu2+exp−x622δu2+exp−x722δu2,uC2x4,x5,x6,x7=0.225exp−1−x422δu2+exp−1−x522δu2+exp−1−x622δu2+exp−1−x722δu2,πC1x4,x5,x6,x7=0.225exp−0.5−x422δπ2+exp−0.5−x522δπ2+exp−0.5−x622δπ2+exp−0.5−x722δπ2,πC2x4,x5,x6,x7=0.(4)
Early warning level of college students’ online public opinionAccording to the four types of college students’ online public opinion, the level of urgency is different. The intuitionistic fuzzy subsets under the early warning domain areZ1 = {levelIV}, Z2 = {level III}, Z3 = {levelII}, and Z4 = {levelI}. Constructing the membership function and intuitionistic fuzzy index of each of the four subsets as follows:(4)uZ1z=0.9exp−z22δu2,uZ2z=0.9exp−1/3−z22δu2,uZ3z=0.9exp−2/3−z22δu2,uZ4z=0.9exp−1−z22δu2,uZ4z=0.9exp−1−z22σu2,πZ1z=0.1exp−1/6−z22σπ2,πZ2z=0.1exp−1/2−z22σπ2,πZ3z=0.1exp−5/6−z22σπ2,πZ4=0,where z∈0,1,σu=0.07,andσπ=0.035.


## 3.1.1. Judging the Network Public Opinion Warning Level

The warning level is determined using the following factors.(1)
Intuitionistic fuzzy subsets reflecting the importance of a particular topicThe importance of a particular topic can be derived from the method described above for calculating the importance of the topic. LetX1∈0,1 be the normalized importance, and record the corresponding domain A = [0, 1]. The membership functions constructing the intuitionistic fuzzy subsets A1=general and A2=general and their intuition indices are each(1)uA1=0.9exp−x122δu2,uA2=0.9exp−1−x122δu2,πA1=0.1exp−0.5−x122δπ2,πA1x1=0.Letδu=0.1416 and δπ=0.035, then the intuitionistic fuzzy set subsets constructed under the domain W can be written separately as x1,uA1,γA1andx1,uA2,γA2.(2)
Intuitive fuzzy subsets representing audience responsesAudience response can be obtained through the combination of the number of audiences participating in the discussion and the speed of the spread of college students’ online public opinion. The membership function of the intuitionistic fuzzy set representing the audience response can be calculated by weighted average using intuitionistic fuzzy comprehensive evaluation. LetX2∈0,1 be the number of normalized audiences, X3∈0,1 be the speed of normalization, the domain that the audience reflects B=0,1×0,1, and the intuitionistic fuzzy subsets under the domain are B1=slow and B2=fast. The subset of intuitionistic fuzzy sets that describe the audience response can be written as x2,x3,uB1,γB1andx2,x3,uB2,γB2. Construct the membership function and the intuitionistic index of the intuitionistic fuzzy subsets, respectively:(2)uB1x2,x3=0.45exp−x222δu2+exp−x322δu2,uB2x2,x3=0.45exp−1−x222δu2+exp−1−x322δu2,πB1x2,x3=0.05exp−0.5−x222δu2+exp−0.5−x322δu2,πB1x2,x3=0.(3)
An intuitionistic fuzzy subset describing the interrelationship between the audience and the topicAnalyze the sentimental tendency of the public in expressing speech, the public’s attention to the college’s online public opinion, and whether the topic is a hot topic, using the weighted average operation in the intuitionistic fuzzy comprehensive evaluation to construct a description of the relationship between the audience and the topic. For the intuitionistic fuzzy subset membership of the relationship, letX4∈0,1 be the normalized attitude bias, X5∈0,1 be the normalized attention, X6∈0,1 be the normalized increase in concern, and X7∈0,1 be the normalized duration of concern. The corresponding intuitionistic fuzzy subset C1=sparseandC2=tight; the intuitionistic fuzzy subset describing the interrelationship between the audience and the topic is x4,x5,x6,x7,uC1,γC1andx4,x5,x6,x7,uC2,γC2. Construct the membership function and the intuitionistic index of two intuitionistic fuzzy subsets, respectively:(3)uC1x4,x5,x6,x7=0.225exp−x422δu2+exp−x522δu2+exp−x622δu2+exp−x722δu2,uC2x4,x5,x6,x7=0.225exp−1−x422δu2+exp−1−x522δu2+exp−1−x622δu2+exp−1−x722δu2,πC1x4,x5,x6,x7=0.225exp−0.5−x422δπ2+exp−0.5−x522δπ2+exp−0.5−x622δπ2+exp−0.5−x722δπ2,πC2x4,x5,x6,x7=0.(4)
Early warning level of college students’ online public opinionAccording to the four types of college students’ online public opinion, the level of urgency is different. The intuitionistic fuzzy subsets under the early warning domain areZ1 = {levelIV}, Z2 = {level III}, Z3 = {levelII}, and Z4 = {levelI}. Constructing the membership function and intuitionistic fuzzy index of each of the four subsets as follows:(4)uZ1z=0.9exp−z22δu2,uZ2z=0.9exp−1/3−z22δu2,uZ3z=0.9exp−2/3−z22δu2,uZ4z=0.9exp−1−z22δu2,uZ4z=0.9exp−1−z22σu2,πZ1z=0.1exp−1/6−z22σπ2,πZ2z=0.1exp−1/2−z22σπ2,πZ3z=0.1exp−5/6−z22σπ2,πZ4=0,where z∈0,1,σu=0.07,andσπ=0.035.


## 4. A Method Based on Bootstrap Estimation

In order to obtain the robustness estimation of the unknown coefficientβ and the nonparametric main part g⋅, we use the bootstrap estimation method to improve the penalty. First, the fitted values β¯,g¯,andεt¯ are the unknown coefficients, nonparametric functions, and residuals from the penalty least squares estimate of yi to xi. For the existing m samples, a random sample is recorded. The bootstrap residual is ε1¯,…,εm¯. Next, generate a new bootstrap sample based on the model yi=xiβ¯+g¯ti+εm¯, and then, estimate based on the newly generated bootstrap sample y=y1,…,yi, thus obtaining the unknown coefficient β and the nonparametric main g⋅ parameter estimation. Finally, repeat the above steps B times to get the final bootstrap parameters and corresponding probability intervals. The multistep probability density prediction method proposed is shown in Figure 1.Figure 1
The proposed semiparametric additive modeling prediction framework.


## 5. Empirical Analysis and Simulation

From the lyric topics collected by Sina Microblog, Baidu Post Bar, and other channels, several college students’ online youth topics were selected: (1) postgraduate fee reform; (2) Fudan poisoning case; (3) college poverty health assessment methods; (4) overseas anticorruption; (5) discrimination of university growth and death; (6) the aunt comics to discourage uncivilization; (7) Shanghai Bund. The data collected by these topics is normalized, as shown in Table1.Table 1
College lyric topics collecting data.
Topicx1x2x3x4x5x6x710.230.920.530.020.710.690.1520.650.710.680.970.600.580.3730.480.840.430.650.330.450.4740.570.890.360.780.390.350.2850.320.250.610.540.180.240.3060.410.250.360.750.120.140.1570.580.870.370.800.340.370.25For the first college student network lyric topic, the input detection vector is (0.23, 0.92, 0.53, 0.02, 0.71, 0.69, 0.15). First, the vector is blurred, and the output is (0.67, 0.82, 0.59, 0.77); it is obvious that the topic has the highest degree of closeness with the warning level (Z2 level), and it is known that Z2 is a media level (medium type). The same reason can be obtained to judge the results of the early warning level of college students’ online public opinion topics. The results are shown in Table2.Table 2
Research results of lyric topics in colleges and universities.
Topicσ1σ2σ3σ4Warning level10.670.820.590.77III20.730.670.700.82I30.760.680.750.64iv40.730.650.780.73II50.810.740.630.70iv60.850.710.640.69iv70.750.660.790.71IIAs can be seen from Table2, the early warning levels of the abovementioned seven college students’ online public opinion topics are moderate, extraheavy, light, heavy, light, light, and heavy. For the special heavy and severe lyric events, for the sake of social harmony and stability, the government needs to take some necessary measures to control the direction of public opinion and prevent the general turmoil caused by the public sentiment. The results of this study are consistent with the actual discussion of the event. The actual social impact and trend of the event are basically consistent with the model prediction results.From January 26th to January 30th, related news such as “Hunan University Transfer Order Cancellation” became a hot topic. Around the relevant public opinion set up in education news, 36,765 positive information, 19,346 negative information, and 26,322 neutral information were recorded. The more the audience, the greater the influence of public opinion. Normalize the number of public opinion audiences to get the valuex2. The higher the x2, the higher the risk level. In this case, the x2 value is 0.87. The trend of attention on the PC side and the mobile site is shown in Figure 2.Figure 2
The trend of attention on the PC side and the mobile side.The main information dissemination platform was news, and it reached the peak of event dissemination on January 26. The number of information dissemination was 458. The incident began to spread from January 21, and the first time the Huda responded was on the January 22, and hence, the attention began to rise sharply. On the 2nd, the official responded for the second time. Due to the inconsistency of its statements, the media and netizens were triggered. With the focus on continuous fermentation, its attention reached its first peak on the 24th. Subsequently, the dissemination and attention of the event began to gradually slow down. By January 30, the Ministry of Education’s investigation results and treatment opinions were announced. As the netizens were dissatisfied with the results of their disposal, the degree of attention rose again, once reaching a secondary peak, and as time went on, attention degree gradually slows down, as shown in Figure 3. After the outbreak of the illegal transfer of postgraduate students in Hunan University, it caused widespread concern. Take Sina Microblog as an example, the topic titled “Hunan University Transfer Event” once jumped to the top of the real-time topic list, with about 6.9 million topic readings, comprehensive analysis, x6 sensitivity and focus degree, and x7 value of topic hotspot sensitivity 0.36 and 0.29. The higher the value of x6 and x7, the higher the danger level. Therefore, the input detection vector is (0.56, 0.87, 0.38, 0.65, 0.35, 0.36, 0.29); first, blur the vector; the output is (0.7368, 0.6814, 0.7925, 0.7136); judging the network public opinion warning level is level II.Figure 3
Variation curve.


## 6. Conclusion

This paper mainly constructs a qualitative and quantitative theoretical model to evaluate college students’ network public opinion warning level. The model is constructed using intuitionistic fuzzy reasoning and a hierarchical analytical process. While using intuitionistic fuzzy reasoning to judge the network public opinion warning level, the topic importance, public reaction, and topic relevance are used as the participation factors. The degree of membership of each factor is calculated by the intuitionistic fuzzy comprehensive evaluation method. The closest intuitionistic fuzzy set is used as the network alert level. The analytic hierarchy process is used for decomposing the target into multiple indicator levels. An expert scoring method is introduced to determine each level of indicators’ weight to determine the final indicator system. After the consistency check of the indicators’ specific weight values at various levels, the network public opinion’s index value can be calculated according to the constructed model, and the warning level to be activated is determined according to the threshold interval corresponding to the index value. At the same time, the model was tested empirically by the stampede event in Shanghai. Empirical studies show that the topic’s importance can be obtained through the membership function and the intuitional real number calculation. The audience reflection can be obtained through the joint result of the number of audiences participating in the discussion and the public opinion’s speed. The weighted average of the intuitionistic fuzzy evaluation is constructed to describe the relationship between the audience and the topic. The analytic hierarchy process can decompose the factors affecting the development of public opinion into 25 indicators that can be observed and measured. By comparing the importance of different levels of elements, each indicator’s specific scores are obtained. The public opinion research index valueS is used to predict the degree of attention. The closer the value of S is to 1, the more serious the whole situation’s development will be.



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*Source: 9979770-2021-05-17.xml*