The mental health of college students is a key focus of societal concern, and constructing a scientific evaluation system is fundamental to achieving effective intervention. This study proposes a method for developing a mental health evaluation index system for universities based on Random Forest (RF) algorithm. First, guided by the biopsychosocial model, 20 secondary indicators were preliminarily established across five dimensions: emotion, self-cognition, interpersonal adaptation, academic career, and behavioral lifestyle. Subsequently, the Gini impurity decrease, and permutation importance criteria of Random Forest were employed to screen the indicators, identifying a core set of metrics and constructing a binary classification prediction model. Empirical results demonstrate that the RF-screened indicator system is concise and effective, with the RF model achieving an accuracy of 0.892 and an AUC value of 0.945, significantly outperforming models such as logistic regression and support vector machines. This study confirms the superiority of Random Forest in handling high-dimensional nonlinear problems in psychological data, providing theoretical and methodological support for universities to establish intelligent mental health warning systems.