College Students’ Mental Health Analysis Based on Clustering Analysis Algorithm

Abstract

AbstractIn recent years, with the expansion of college enrollment, employment difficulties and other social pressure reasons, resulting in the multiplication of College Students’ mental health problems. The traditional way of analyzing the mental health of college students is not enough. Big data and intelligent tools for mental health analysis is the future development trend, but it is rarely used in Colleges and universities at present. Therefore, this paper proposes the research of College Students’ mental health analysis based on clustering analysis algorithm. This paper makes an in-depth analysis of the main mental health problems and influencing factors of college students in China, and classifies them according to their characteristics. In this paper, according to the characteristics of College Students’ mental health problems, combined with clustering analysis algorithm, the analysis model of College Students’ mental health problems is established. This paper gives the steps of clustering analysis of College Students’ mental health problems in detail, and optimizes and improves the algorithm, which improves the performance and robustness of the algorithm. In order to further verify the actual effect of this model, this paper makes relevant investigation experiments, taking 3625 students of a university as the research object. The survey results show that 87.5% of the students are clustered into 1 category, which indicates that most of the students in this school have good mental health. The analysis shows that the test results are in line with the actual situation, and the actual effect of College Students’ mental health analysis model based on clustering analysis algorithm is verified.KeywordsClustering analysis algorithmMental health analysisPsychological ProblemsData mining technology