Analysis and Prediction of College Students' Hidden Mental Health Status Based on Data Mining

With the rapid development of social economy and the continuous reform of education system, the pressure and competition faced by college students continue to increase, so that the mental health problems of college students are becoming increasingly serious. Among them, hidden mental problems are difficult to identify, imperceptible, sudden and serious, which has become an important challenge for mental health education in colleges and universities. However, at this stage, the selection of mental health assessment tools and the formulation of assessment process in colleges and universities are lack of scientific and normative, and lack of necessary technical support, so that the accuracy of assessment results is insufficient, and it is difficult to play the actual effect of early warning mechanism. In this regard, based on the actual needs, this paper will focus on analyzing the practical advantages and feasibility of big data analysis and data mining technology, and put forward a set of analysis and prediction scheme of hidden mental health status of college students based on data mining, in order to provide scientific basis and personalized intervention strategies for mental health education in colleges and universities. Practice has proved that by collecting and analyzing the relevant data of college students, XGBoost prediction model is established by using data mining algorithm, and accurate evaluation and early warning of hidden mental health problems of college students can be realized, so as to better protect students' mental health.