Algorithms for assessing and predicting the mental health of college students based on big data analysis

Abstract

This paper builds a system for assessing and predicting the mental health of college students based on big data analysis. The aim is to conduct risk assessment and trend prediction through the integration of multiple data sources and time series prediction models. The system employs a multi-source feature fusion algorithm, including source normalization, weighted fusion, and unified representation learning methods, to prevent a single data source from dominating the results during the fusion process. To account for the inertia and recoverability of mental health, the system introduces a time state update algorithm with an forgetting factor to describe the accumulation and attenuation of risks over the time axis. This mechanism can dynamically adjust the weight of historical information in the prediction, thereby coping with the volatility of mental health risks. In the risk assessment stage, the system converts the linear score into a probability using the Sigmoid function and maps it to the [0, 1] interval. Based on this, it performs hierarchical classification according to the set threshold, clearly defining low, medium, and high-risk levels. Through this series of algorithms, the system not only can predict the risk trend in future time windows but also effectively identify the turning points of risk increase, and trigger alerts when the risk level exceeds the preset threshold or shows continuous fluctuations, supporting early intervention and resource allocation. The design and implementation of this system provide a precise and operational technical solution for the management of college students' mental health.