Multi-source Data Driven Mental Health Prediction Method for College Students
WANG Fang
ZHAO Xiaoming
HUO Ningning
Abstract：Aiming at the current situation of psychological problems existing in college students,multi-source data driving is used to predict the mental health of college students.Firstly,part of college students'life data is mined,multi-source data set of col-lege students'mental health prediction is designed,and relevant features are extracted according to the data form.At the same time,aiming at the problem of too many features brought by multi-source data sets,the Relief algorithm is optimized and integrated for feature selection,and finally lightGBM classifier is used to complete efficient and accurate mental health prediction.This method is used to analyze and predict the data of students.The experimental results show that the method can realize the effective identifica-tion of students'psychological problems,so as to provide the planning and decision-making basis for the mental health education of students in universities.
Keywords：mental healthcollege studentsprediction methodmulti-source dataRelief algorithmlightGBM algorithm
Publication Date：2025-12-20
Online Publishing Date：2026-03-09(First online date of this platform, not the publication date of the document)
Pages：6( 3337-3341,3500 )