Application of K-Means Clustering Algorithm in Analyzing College Students' Mental Health

The purpose of this study is to explore the application of K-means clustering algorithm in analyzing college students' mental health. By collecting the data of college students' mental health evaluation, this paper divides the samples into different clusters by using K-means algorithm, and analyzes the characteristics of each cluster. The results show that there is diversity in college students' mental health, and there are significant differences in mental health indicators such as anxiety and depression among different groups. By calculating the contour coefficient and other indicators to evaluate the clustering effect, it is found that the selected clustering number can well divide different mental health groups, and the clustering results have certain stability and reliability. This study provides a new method and perspective for understanding college students' mental health problems, and provides scientific basis and reference for mental health evaluation and intervention.