Acceso usuarios
Yajing Pang
In recent years, the psychological problems of college students have attracted extensive attention. It is of great practical significance to timely and arcuately intervene in college sports majors faced with psychological crisis (PC). However, the existing studies mainly analyze the mood and psychological state at a certain moment, but rarely track the psychological health state of different types of college students. This paper proposes a way to intervene and predict the PC of college sports majors based on big data analysis. Firstly, the massive evaluation data were collected from a psychological census database on PC of college sports majors and subjected to data mining. Besides, a PC evaluation model was established based on the decision tree (DT) algorithm. Next, the behavior big data of college sports majors in social network were fully utilized, and a PC intervention and prediction method was developed based on social network readme texts. Further, the authors extracted features from readme texts, evaluated the level of PC risk, and analyzed the longitudinal features. Finally, the proposed model was proved valid through experiments. This paper effectively applies new technologies to the data mining of the typical behaviors of college sports majors, and thereby realizes accurate PC warning. The research results are of great significance to improving the psychological health of college students.
