运筹与管理2024，Vol.33Issue(12)：210-216,7.DOI:10.12005/orms.2024.0407
基于大数据的大学生心理精神状况分析与预测
Analysis and Prediction of College Students'Mental State Based on Big Data Analysis
摘要
Abstract
With the rapid development of society,the increase in cost of living and pressure of study and work,the psychological problems of college students become increasingly prominent.At present,teenagers all over the world have psychological problems to a certain degree.College students'psychological state affects all aspects of study and life,and the impact is increasingly intensified.Mental problems can also lead to physical problems such as physical pain and visual fatigue.Therefore,it is the primary task of schools and families to accurately identify college students with psychological problems and improve their psychological quality and anti-pressure ability.Applying big data technology to the field of students'psychology and spirit can collect and analyze all aspects of students'information,dynamically track students'behavior,and capture abnormal information and behavior in real time,so as to intervene in time. Based on SCL-90 scale,a questionnaire is designed to investigate the psychological states of college students.In this paper,five subscales-depression,anxiety,compulsion,paranoia and interpersonal sensitivity,are selected to analyze the psychological states of college students.According to the score of the scale,the psychological states are divided into three grades:weak,general and strong.The frequency of visual fatigue and physical pain under each grade is analyzed,and the relationship among visual fatigue,physical pain and the five mental states is analyzed by the Chi-square test.Four prediction methods,namely decision tree,random forest,multi-layer perceptron and support vector machine,are used.Particle swarm optimization is used to optimize kernel function parameters of support vector machine to improve the prediction accuracy. (1)14%-32%of the students have a general degree of depression,anxiety,etc.,more than 70%of whom suffer from visual fatigue.3%-10%have strong symptoms of depression,compulsion and other symptoms,80%of whom suffer from visual fatigue.Psychological conditions such as depression,anxiety,compulsion,paranoia and interpersonal sensitivity play an important role in the eye fatigue.(2)The proportion of physical pain among students with general mental condition is 26%-44%;among the students with strong psychological condition,the prevalence of physical pain reaches 40%-70%.Psychological and psychiatric conditions also have significant effects on physical pain.(3)Gaussian radial basis kernel function is used in SVM model,and kernel function parameters are optimized based on particle swarm optimization algorithm.The prediction effect of the optimized SVM on the five subscales is better than that of the other four methods. However,limitations still exist and more work remains to be done in the future.On the one hand,the current use of the international general psychological scale may not be completely suitable for Chinese college students.The development,design and application of the psychological scale for Chinese college students need to be improved.On the other hand,the design of some questions in the scale is subjective,and the main data come from questionnaires,so the objectivity and accuracy of the data may be affected.Future research can consider the following three points:Firstly,based on the latest research on college students'psychology,combined with the psychological survey report of Chinese college students,we should set up a psychological measurement system suitable for the characteristics of Chinese college students.Secondly,the objective questions in the scale can be increased,such as the time and number of sports per week,number of visits to the library,and number of absence.Finally,focused on the social media used by college students,data analysis method is adopted to analyze the high-frequency words used by college students in mainstream media,so as to analyze their potential psychological state.关键词
大数据/大学生心理精神状况/机器学习/支持向量机/粒子群算法Key words
big data/mental state of college students/machine learning/support vector machine/particle swarm optimization分类
信息技术与安全科学引用本文复制引用
李清,邓国英,苏强..基于大数据的大学生心理精神状况分析与预测[J].运筹与管理,2024,33(12):210-216,7.基金项目
国家自然科学基金面上项目(71972146) （71972146）
上海市哲学社会科学规划教育学青年项目(B2024001) （B2024001）
