Identification and Early Warning of College Studentsxe2x80x99 Psychological Crisis Based on Big Data Nowadays, the application and popularization of new media such as Weibo, WeChat and QQ make big dataxe2x80x99s age come quietly, which will have a great influence on peoplexe2x80x99s life style, personal thought and behavior, etc. On the basis of analyzing the massive data of college students, this paper designed and developed the prototype system of big dataxe2x80x99s analysis for college studentsxe2x80x99 mental health education, predicted the tendency of studentsxe2x80x99 psychological behavior and constructed the individualized emergency management mechanism of college studentsxe2x80x99 psychological crisis, so that to enhance the realtime and effectiveness of the management of psychological crisis in colleges and universities, and to innovate the mental health education of college students.

Predicting the students with mental health risk by using Internet access logs Nowadays, the mental health problems of college students in our country are becoming more and more prominent. The mental health problems of college students not only hinder their healthy growth, but also affect the social and economic development of our country. Predicting studentsu0027 mental health is an important field in educational data mining (EDM). However, it is very difficult to predict studentsu0027 mental health because of many complex factors that affect the studentsu0027 mental health, so currently there is little research on this field. As the Internet has almost become an essential part of studentsu0027 life, the studentsu0027 Internet use can reflect the studentsu0027 psychological situation to some extent. Therefore, this study analyzes the online log of the freshmen students majored in computer in a university, and proposed an effective method to estimate the studentsu0027 online time. Then, predict the students with mental health risk by using the studentsu0027 online time on different types of Internet as features. The experimental results show that the proposed method is with high effectiveness and can predict about 50% of the students with mental health risk.