Abstract
Aiming at the problem that the mental health of contemporary college students is affected by many parties, and the evaluation accuracy and evaluation effect are poor, an online evaluation method of college students’ mental health based on campus network text mining is proposed. Mining and analyzing the association rules of the association features between texts, analyzing the monitoring quantitative index of the online evaluation of college students’ mental health; based on the regional transfer coefficient of existing mental health text information, the influencing factors of college students’ mental health are determined, and the online evaluation model of college students’ mental health is constructed. The membership degree of the factors is comprehensively calculated, and the corresponding fuzzy consistent matrix is used to compare the quantitative results of the index scales, and the interval number judgment matrix is constructed, which corresponds to the interval number weight of each secondary evaluation index one-to-one. The weight of the indicators is divided into the mental health assessment level, and the online assessment of college students’ mental health is completed. The experimental results show that the evaluation accuracy of the design method is more than 97.36%, the recall rate is more than 97%, and the highest packet loss rate is less than 0.3%, which proves that the online evaluation results of college students in this method are more accurate and that the accuracy and effect of the online evaluation of college students’ mental health are improved.
1. Introduction
With the attention of the country and the urgent needs of reality, almost all colleges and universities have opened mental health courses and established mental health centers, which have shown certain results, but there are also some shortcomings and misunderstandings [1]. Most colleges and universities still adhere to the traditional concept of mental health education, which is to focus on psychological problems and mental illnesses, focus on treatment and neglect courses, pay too much attention to a small number of students with psychological problems, and demand scientific guidance for the development of mental health for most students. The neglect is in stark contrast. In this context, more colleges and universities put most of their resources and manpower into counseling, believing that the top priority of mental health education is to ensure that students do not experience malignant events arising from psychological problems, while ignoring the mental health of college students’ basic situation.
In this regard, it is necessary to actively mine mental health information of college students, that is, to apply data mining algorithms to the process of campus network information extraction [2]. Many scholars have carried out related research, such as Reference [3] shows how the pattern classification problem of attributes selects a small number of simple fuzzy if-then rules, generates candidate rules through the rule evaluation method in data mining, selects the rules through multiobjective evolutionary algorithm, and generates candidate fuzzy if-then rules from numerical data. Two rule evaluation metrics (namely, confidence and support) are used for prescreening in mining, and an idea is proposed to use these two rule evaluation metrics as prescreening criteria for fuzzy rule selection candidate rules, which can generate arbitrary rules from numerical data number of candidate rules, extending the multiobjective genetic algorithm (MOGA) in previous studies to a multiobjective genetic local search (MOGLS) algorithm, where the local search process adjusts the selection (i.e., inclusion or exclusion) of each candidate rule. Reference [4] applied the data analysis-based algorithm to the knowledge mining task in the student data set and benchmarked the k-means algorithm with 22 distance functions based on the contour index, Dunn index, and partition entropy internal validity measure. The hierarchical clustering algorithm was benchmarked by calculating the co-correlation coefficients for different combinations of distance and linkage functions, and the fuzzy c-means algorithm was benchmarked with partition entropy, partition coefficient, silhouette index, and modified partition coefficient, applying. The k-nearest neighbor algorithm determines the optimal ε value for spatial clustering of density-based noise applications, demonstrating its effectiveness in the knowledge mining task in the student engagement dataset. In order to strengthen the overall security of the network, Reference [5] analyzes the network security issues, studies the network security based on the prefix spanning algorithm of data mining, and proposes the classical data mining algorithm, prefix spanning algorithm, and its improvement. Based on the characteristics of security, this algorithm is applied to network security intrusion detection. Starting from the algorithm process and evaluation model, an optimization and update scheme is proposed. Through the effective evaluation of the data analysis state, an effective data transmission evaluation model is established. In pattern mining, the algorithm has more advantages and can better meet the high requirements of intrusion detection. On the basis of data mining, college students’ mental health evaluation has been widely valued by psychologists and educators. Reference [6] proposed the design and application of an automatic assessment model for college students’ mental health based on multimodal data fusion. Based on the theory of ecological instantaneous assessment, an automatic assessment model of college students’ mental health based on multimodal data fusion calculation is constructed; using the deep learning algorithm to solve the model, the automatic evaluation results of college students’ mental health are obtained. This method does not comprehensively analyze the influencing factors of college students’ mental health, resulting in poor accuracy of assessment. Reference [7] proposed a psychological evaluation method for college students based on krill swarm algorithm to optimize support vector machine, aiming at the influence of penalty coefficient and kernel function parameters on the performance of support vector machine. According to the total score of SCL-90 and the evaluation guide of China’s conventional model, the kh-svm evaluation model of college students’ mental health is established by taking the 9 dimensions of evaluation indicators such as somatization, interpersonal sensitivity, psychosis, depression, paranoia, terror, hostility, anxiety, and compulsion as the input of the kh-svm evaluation model and the mental health status of college students as the output of the kh-svm evaluation model, in which the mental health status of college students is divided into unhealthy, mildly unhealthy, and healthy. This method has a poor evaluation effect on single mode data, and the evaluation result has a large error.
In order to improve the accuracy and effect of online evaluation of college students’ mental health, an online evaluation method of college students’ mental health based on campus network text mining is proposed. The proposed evaluation method mines and analyzes the association rules of association features between texts, and analyzes the monitoring quantitative indicators of online evaluation of college students’ mental health. Different from the existing research results, this paper fully considers the influencing factors of college students’ mental health, creatively determines the influencing factors of mental health based on the regional transfer coefficient of the existing mental health text information, and constructs an online evaluation model of college students’ mental health. The membership degree of each influencing factor is comprehensively calculated, and the quantitative results of each index scale are compared with the corresponding fuzzy consistent matrix, and the interval number judgment matrix is constructed. Divide the index weight into mental health assessment levels and complete the online assessment of college students’ mental health. The experimental results show that the research contribution of this method is to improve the accuracy and effect of online evaluation of college students’ mental health and to provide reliable data reference for college students’ mental health education.
2. Materials and Methods
2.1. Correlation Characteristics between Campus Network Texts
The apriori algorithm in the association rule mining algorithm is used to mine the association rules in the campus network text. The frequency of the campus network text attributes is set to be greater than or equal to the minimum support degree, and then, strong association rules are generated from the frequent set. These rules must meet the minimum support degree and minimum confidence [8, 9]. Using a recursive method, all rules containing only frequent set attributes are generated. Once these rules are generated, only those rules greater than a given minimum confidence level are left.
According to the above principles, the attribute support rate of the campus network text candidate item set is . If is greater than or equal to the minimum support rate given by the user, this is recorded as , that is, represents frequent itemsets; otherwise, represents infrequent item sets. Suppose and , both within , if , then , and represents infrequent itemsets and also represents infrequent itemsets; if , then represents frequent itemsets and also represents frequent itemsets.
The strong association rule for frequent itemsets is of the form . The premise of is that , , are all itemsets, and , , and at the same time.
The properties of strong association rules are described by using the support degree of and the confidence degree of 2 parameters, namely, , and refined as:
Generally, only when the confidence is greater than the expected confidence, has a promoting effect on the appearance of , that is, there is a correlation between the two. The task of association rules is to dig out all the strong rules in , so that the corresponding itemsets 66 of the strong rules must be frequent itemsets [10, 11], and the association rules calculated by the frequent itemsets are confident degree, which can be obtained through the support degrees of frequent itemsets and , so as to find all frequent itemsets, whose support degrees are higher than the least support itemsets. The data association features mined by the association rules are used as the basic data of the campus network text, and the campus network text set is searched and iterated layer by layer to mine the rules. Specific steps are as follows:(1)when scanning the campus network text database for the first time, calculate for each item set in the itemset and find the frequent itemset that satisfies .1.(2)Use to generate frequent itemsets . The frequent itemsets generated after the -th scan are used as the mining rule seed, and set to connect the candidate rule itemsets associated with potential attributes. Then scan again, recalculate the support of all items in [12], and then find out all frequent itemsets from that satisfy the condition of , and will be used as a subset for the next scan. The above process is repeated until no new frequent itemsets are generated, that is, is an empty set. All previously generated itemsets are associated features.
3. Deduplication and Segmentation of Frequent Itemsets
The apriori algorithm obtains the final frequent itemsets through continuous iteration, and there are too many candidate itemsets in the search process, which have high complexity [13]. When the apriori algorithm is directly applied to the massive database of the campus network, it has low operation efficiency and is prone to excess memory. Therefore, Boolean matrix is introduced into the apriori algorithm. Because Boolean matrix can simplify the frequent itemsets of the apriori algorithm, it can be used in campus network text mining to segment the database and then segment the segmented database, which can improve the operation efficiency and reduce the memory occupation.
The process of obtaining frequent itemsets using the apriori algorithm optimized by Boolean matrix is as follows:(1)set the number of copies of the massive text database to be divided and determine the size of the different copies to be divided. Initialize the loop variable to 1 and set the minimum support for the apriori algorithm.(2)Read the text database and map it to the Boolean matrix .(3)Calculate the local minimum support of against using the following formula [14–17]: In the formula, and represent the number of elements in the text database and the number of elements in the massive text database, respectively, and represents the amount of data in the campus web text dataset containing mental health information. Assuming that there are text databases that have been segmented, denoted by , it can be seen that there are Boolean matrices in one-to-one correspondence with the segmented text databases. Obtain the corresponding row vector of the frequent itemsets in in the Boolean matrix by formula (1), save the row vector obtained by searching, release the memory space of the Boolean matrix to update the data set, and obtain the updated matrix .(4)Set , when the condition of is satisfied, go back to step (2) to repeat the iterative calculation; otherwise, go to step (5).(5)Recombine the corresponding Boolean matrices of all frequent itemsets in the text data set and establish a new Boolean matrix represented by . The minimum support degree of the established new Boolean matrix is searched again, and the corresponding row vector of the frequent itemset of the massive text database is determined, and the frequent itemset that can finally evaluate the mental health of college students is obtained.
4. College Students’ Mental Health Text Information Collection
The text information of campus network is analyzed, and the association rule fusion set of campus network text information is constructed. Its expression is as follows:
In the formula, is the correlation parameter of text information, is the correlation feature value between texts, and the campus network text data set containing mental health information is .
By mining and analyzing the association rules of the association features between texts, the feature offset coefficient of the online assessment of college students’ mental health is obtained as follows:
In the formula, is the offset coefficient of text information and is the monitoring quantitative index of online assessment of college students’ mental health.
Under the condition of ensuring the stable operation of the campus network, the data with a large difference in average value in the data stream are eliminated to avoid data pollution. After that, the mental health information collection frequency is set as follows:
In the formula, is the virtual evaluation value.
In the area where mental health text information exists, the calling coefficient is , and the range of the area where mental health text information exists is as follows:
In the formula, is the mental health text information matching function and is the text information distribution coefficient.
Through the above-determined text information feature quantity, as the association rule of online assessment of college students’ mental health [18–20], the online assessment model of college students’ mental health is constructed in the data layer of campus network:
In the formula, is the density distribution function in region of online assessment of college students’ mental health.
Since the research object is network data and its types are complex, it is necessary to deal with outliers in the data stream to provide a guarantee for the accuracy of data classification [17, 21–23]. Through the calculation of the online evaluation model of college students’ mental health, combined with , we can obtain college students text information detection function for online mental health assessment:
In the formula, is the feature set of the text information of the online assessment of college students’ mental health and 2 is the distribution feature vector of the text information of the online assessment of college students’ mental health, which is expressed as the following:
Convert text data into machine-recognized text and store it in vector space to complete the data stream preprocessing process. Set the collected text set as a vector in an -dimensional space, that is, this text data has features, and each piece of information in this data set can be mapped to a point in the space model, represented by one of thevectors; then and ; in this study, represents the weight of data feature in documents. At the same time, the weight of the feature words in this article is measured by , and the calculation rules are set as follows:
In the formula, the number of times the feature value appears in the text can be expressed as , is the text data with the feature value , and is the total number of texts in the set.
5. Online Assessment Method of College Students’ Mental Health
5.1. Confirm the Influencing Factors
In the actual work of mental health education, many specific and challenging problems are often faced. We will analyze them concretely and extract the specific factors that affect the mental health of college students as the influencing factors of online assessment of college students’ mental health.(1)Teachers who undertake this work need to have a solid professional knowledge reserve and a strong working ability to deal with practical problems. Psychological consultants need continuous re-learning and training to deepen their knowledge system and further realize their professional growth, especially for some psychological teachers who have just entered their careers, although they have received professional and systematic learning at the undergraduate or postgraduate level. However, there is a relatively lack of specific operational skills and practical experience, so appropriate training and learning are needed to promote the improvement of their own abilities, and some psychological teachers who have worked for many years will also feel different degrees of professional fatigue. The specific cases and changing learning needs of students feel overwhelmed by the lack of growth and timely updating of their existing professional knowledge.(2)In the university system, the degree of professionalism of college students’ mental health courses is relatively low. There are relatively few trainings, specifically for mental health, and most of the trainings are short-term, and the specific training content is also very limited. It is difficult to bring substantial professional growth and improvement to the students participating in the training. At the same time, the training qualifications of institutions are uneven, the quality of training is difficult to guarantee, and the degree of professional and technical improvement is limited. In addition, the type and quantity of training that teachers can participate in are small, the form is single, and the reimbursement of relevant training funds is limited. The inability to participate in high-paying, long-term, high-quality training has largely restricted the professional development and ability improvement of psychology teachers.(3)There are no relatively unified teaching materials and effective teaching methods for mental health courses. Existing textbooks are basically the same, mostly statements of theoretical knowledge and lack of innovation and interest, and it is difficult to stimulate the desire of contemporary college students to learn. In addition, most colleges and universities adopt the traditional teaching method of cultural courses, and teachers use knowledge and principles. Mainly speaking, students digested and absorbed it and did not participate too much in it and could not really mobilize students’ interest and desire for the learning content. The classroom atmosphere was dull, and it was difficult to achieve the proper teaching effect.(4)The division of labor between curriculum and counseling in mental health education is not clear. The work of mental health education mainly includes two parts: curriculum and counseling. Although they belong to the same system, the work focus, work methods, and required knowledge structure of the two are different. The course is mainly to help students form a healthy and sound personality, focusing on the development of positive psychological qualities, and how to maintain a good attitude, correctly face pressure and setbacks, and maximize students’ psychological potential. Psychological counseling is to conduct certain psychological counseling and intervention for a small number of students who have a certain degree of psychological problems and identify whether the psychological problems are beyond the scope of psychological counseling in colleges and universities and whether they need to be referred to professional institutions. Although the institutional settings of various colleges and universities are different, most colleges and universities have set up mental health centers in the students’ office due to practical conditions, and most of the teachers in the students’ office belong to management positions. In administrative work, the division of labor is chaotic and unscientific. Even if there are a small number of teachers who belong to the teaching post, the work done by the teachers in the management post is not much different from that of the teachers in the management post. There is no way to focus on teaching and scientific research like other subject teachers. The teaching effect is not ideal and is not conducive to mental health. The scientific and systematic formation of the educational model is also difficult to play its due role.
5.2. Calculate the Membership Degree
According to the influencing factors that may affect the mental health of college students, the membership degree is comprehensively calculated, and the specific factor indicators are shown in Figure 1.
5.2.1. Influence Factor Membership Degree Matrix
The calculation process of the membership degree of college students’ mental health influencing factors applied in the FAHP model is as follows: assuming that there are event influencing factors, which are levels , and then the order fuzzy matrix of the membership function matrix of the influencing factors is expressed as:
5.2.2. Establish a Weight Set
The membership matrix of the influencing factors can comprehensively evaluate the security threats that college students may face to their mental health. Here, the comparison values of different event factors are further calculated, and the corresponding fuzzy consistency matrix is used to compare the quantitative results of the index scales to obtain a fuzzy judgment matrix. Influence value of index consistency: , and are, respectively, described as the actual measured data value of the drilling safety assessment factor index and the arithmetic mean of different levels of standards, and is the weight value of the parameter.
In order to ensure the accuracy of the fuzzy operation, it is necessary to normalize each factor in the weight matrix that can affect the operation of the equipment and obtain the corresponding parameter weight value:
In the formula, is described as the normalized weight value of .
In addition to genetic factors, the inner world of college students is mainly the long-term cumulative effect of various external factors in the past growth process, so external factors can be transformed into internal factors over time. The matrix assigns value to the influencing factor data and obtains the fuzzy judgment matrix , which contains . Assuming that occurs, then the definition is the fuzzy consistent matrix.
According to the influencing factor indicators, the eigenvectors corresponding to the factor indicators can be calculated, and when the vector values fully satisfy the matrix consistency, the matrix can be formed, and is described as the weight set .
5.2.3. Compound Operations of Fuzzy Matrices
According to the above calculation, the composite operation is performed on the weight set of data factors affecting the mental health of college students and the membership function matrix, and we can get the following:
In the formula, represents the composite operation result, and the combination of the weight set and the matrix can obtain the second-level fuzzy analysis judgment, and then the calculation results will correspond to the membership degrees of the influencing factors at different levels.
5.3. Online Assessment Model of College Students’ Mental Health
By replacing the point value, the weight of the interval number is calculated, and then the original data and results are calculated, and they are all represented by the interval number. Suppose represents a judgment matrix of interval numbers, and the expression of is , denoted , , , and can be represented as for the interval number vector. Assuming that is given, the steps to calculate the weight of the interval number of the online evaluation index of college students’ mental health impact factor using time series are as follows. Step 1: use the time series to find the sum of the eigenvectors corresponding to the maximum eigenvalues of and , respectively; Step 2: calculate and according to the maximum eigenvalues of and . The formula is as follows: Step 3: calculate the interval number form weight vector of the online evaluation index of college students’ mental health impact factor .
According to the time series, the method of calculating and evaluating the weight of the indicators adopts statistical management to form a unified standard for the confirmation of the impact factors of college students’ mental health and then generates a judgment matrix of interval numbers. The quantification results of the impact factors of the secondary indicators are shown in Table 1.
According to the interval number of the first-level indicators in the online assessment of college students’ mental health in Table 2, the interval number weight vector of the first-level evaluation indicators in the college students’ mental health evaluation system can be calculated, and at the same time, the interval number weights of each second-level evaluation index can be obtained, as shown in Table 2.
Due to the complexity of the online evaluation index system of college students’ mental health impact factor, it is difficult to quantify the qualitative evaluation indicators. Using the interval number and time series principle, the interval number weight of the online evaluation of college students’ mental health impact factor is calculated, and the mental health of college students is determined.
Divide the multiattribute parameters into soft parameters and hard parameters. For the hard parameters, according to the specific situation of campus network text mining, refer to the actual data to determine the difference between the two scoring levels. For the soft parameters, the quantitative standard is used. Measure and qualitatively describe the attribute parameter scores. According to the weight vector and the score vector of the second-level parameters, the first-level parameter score vector is calculated, and the formula is the following:where represents transposition. Then, the evaluation value of mental health level is as follows:
In the formula, is the first-level attribute parameter weight. According to the final evaluation value, the mental health evaluation level is divided as shown in Table 3:
According to the content shown in Table 3, the multiparameter evaluation level of mental health was determined, and the online evaluation of mental health of college students was completed.
6. Experiment
We precollected 155,646 campus network social texts and 15,844,152 vocabulary words published by college students of a certain school within a period of time, selected the public sentiment dictionary constructed by American scholar Bing Liu, and collected campus network text information in the sentiment dictionary. The proportion is 42% %, delete useless evaluation texts and junk evaluation texts, collect a total of 2000 valid evaluation texts, of which 1000 are texts with known mental health problems, as online evaluation samples, and input them into the online evaluation model of college students’ mental health, as follows:(1)the calculation of all itemsets is represented by , and search for all commonly used itemsets greater than or equal to the minimum support that has been set and represented by .(2)Use the commonly used 1-itemset to obtain the candidate 2-itemsets, which is represented by . From the acquired 2-itemsets, search for all 2-itemsets greater than or equal to the set minimum support degree and denote by .(3)According to the above process, the candidate 3-itemset is obtained by using the acquired common 2-itemset, which is denoted by . Search for all 3-itemsets greater than or equal to the set minimum support from the acquired 3-itemsets and denote by .(4)Repeat the above process of iteration until the frequent items of higher dimension cannot be obtained and terminate the iteration.
The grid search method is selected to determine the hyperparameters, and the results are shown in Table 4.
According to Table 4, in order to visually demonstrate the evaluation effect of the method in this paper, the above 1000 texts of known mental health problems were taken as the experimental object, and the method in this paper was compared with the model of references [6, 7], and the results were compared, as shown in Table 5.
It can be seen from Table 5 that the method in this paper is aimed at different campus network text information, and the results of the mental health assessment of college students have an evaluation accuracy of 97.36% and a recall rate of 97.16%. Compared with other methods, this method can improve the evaluation accuracy and recall. The weight of the interval number of the factor online assessment, one by one, the soft parameters are obtained in a targeted manner, and the attribute parameter scores are qualitatively described to complete the online assessment of college students’ mental health.
The switching delay is controlled at about 2s, and after switching the feature vector corresponding to the factor index, the evaluation model will receive data packets containing a variety of college students’ mental health text information. The formula for calculating the packet loss rate is as follows:
In the formula, the total number of packets sent and received is represented by and , respectively, as shown in Figure 2.
As can be seen from Figure 2, the highest packet loss rate of the method in Reference [6] is about 0.47%, the highest packet loss rate of the method in Reference [7] is about 0.45%, and the highest packet loss rate of the method in this paper is about 0.3%. This is because this method first excavates and analyzes the correlation features and correlation rules between texts, introduces the Boolean matrix in the apriori algorithm, and divides the massive campus network text database; thus, eliminating the data with the large difference in the data flow average, reducing the interference of redundant data, and thus ensuring the low packet loss rate.
7. Results and Discussion
In order to improve the accuracy of contemporary college students, this paper proposes an online assessment method of college students’ mental health based on campus network text mining, explores various influencing factors of college students’ mental health, uses the apriori algorithm optimized by Boolean matrix to obtain frequent itemsets, and calculates the mental health of college students in the FAHP model. The membership degree of the impact factor and the comparison value of different event factors are calculated, the corresponding fuzzy consistent matrix is used to calculate the eigenvector corresponding to the factor index, the time series is used to calculate the weight of the interval number of the online evaluation index of the mental health impact factor of college students, and the attribute parameter is qualitatively described. The experimental data show that the online evaluation result of college students’ mental health proposed in this paper has a high accuracy, which improves the online evaluation effect of college students’ mental health. However, due to the limited conditions, this paper has not significantly improved the efficiency of college students’ mental health evaluation. Future research can reduce the evaluation time and improve the evaluation efficiency on the basis of ensuring the evaluation accuracy.
Data Availability
The authors confirm that the data supporting the findings of this study are available within the article.
Conflicts of Interest
The authors declare that they have no conflicts of interest.
Acknowledgments
This study was supported by special project on Ideological and Political Education for College Students in Zhejiang Province was Launched in 2019: Investigation and countermeasures of mental health literacy of higher vocational students from the perspective of Lide Shuren (Y201942528); Ideological and political project funding project of Wenzhou Universities: An Empirical Study on the Intervention of Quality Outward Development Training on Internet Addiction Behavior of College Students (WGSZ202106); and Wenzhou Philosophy and Social Science Planning Project in 2020: From the perspective of demand theory: “on demand funding” development funding model construction and Practice Research (20wsk265).