Prediction of College Students’ Psychological Crisis Based on Data Mining

Abstract

The development of a college students’ psychological management system has become an essential indicator to monitor and prevent the psychological crisis. University student management databases accumulate massive data, but the conventional data processing tasks are restricted to simple statistical analysis, storage, and query management. This paper discusses the application of big data technology for the current psychological management system by investigating psychological crisis screening indicators. Data mining techniques are used to realize the dynamic management of psychological early warning data, real-time monitoring of high-risk groups’ psychology, and improvement of the accuracy and effectiveness of early identification and warning of students’ psychological crisis. Based on a combination of qualitative and quantitative analysis, we conduct a series of studies on three typical types of network public opinions, i.e., Internet rumors, online public opinions of college students, and emergent public health incidents in terms of the transmission mechanism, early warning, and decision-making mechanism, as well as the evolution mechanism of the network public opinion.