Article Preview
Top1. Introduction
Due to the increasingly fierce social competition, college students inevitably face and bear various pressures (Shervington et al, 2020). College students are in the transition stage of school and social roles, and are often faced with the challenges of learning, employment, love, family, and other aspects. The internal transformation and external development are not balanced, making students' psychological adjustment and self-healing issues call for more attention (Yang ZL et al, 2022).
In recent years, malignant events caused by psychological problems have occurred frequently (Ben-Yehuda et al, 2022). As far as depression is concerned, the report on the development of China's national mental health (2019~2020) published by the team of literature (Wang, 2019) that 13.6% of people with undergraduate degrees and above suffer from depression, and 4.2% suffer from severe depression. Therefore, it is the key to put forward and formulate psychological early-warning methods for colleges and universities, and to increase the attention to the psychological health of college students.
Timely and accurate early crisis warning is likely to save more lives. How to accurately identify the crisis, receive the early warning in time, and take effective measures is the key to achieve an early warning of psychological crisis (Guan et al, 2020).
In most colleges and universities, the way to study and judge the psychological state of college students is still relatively traditional, lacking efficiency and accuracy (Antunes-Alves et al, 2021). The traditional psychological intervention methods mostly adopt a psychological questionnaire screening, lectures and post psychological treatments. However, these practices cannot effectively and dynamically grasp students' psychological conditions in a timely manner, so as to timely intervene in possible crises (Yang & Fu, 2022).
With the rise of social networks, researchers have conducted data mining and analysis on user data and behavior on social networks, which is helpful to understand and predict users' psychological state (Tadesse et al, 2019). Reference (Mitja, 2010) shows that personal information of social networks can reflect the real personality of individuals. Research in reference (Youyou & Kosinski, 2015) shows that computers can make more accurate judgments about people's personalities based on Facebook data than people do. Therefore, big data analysis technology is applied to the analysis of mental state, and a new solution is proposed for college students' psychological crisis intervention research (Jia et al, 2021).