A study was conducted to analyze students’ mental health problems based on the Student Mental Health Index evaluation results and develop a monitoring and early warning method for student mental health data using IoT. Within the IoT sensor architecture, student mental health data performance was assessed, and targeted processing was performed to analyze the evaluation data. By constructing a data matrix and designing monitoring and warning methods based on frequent monitoring items, the study successfully evaluated the results, identified specific mental health issues faced by individual students, and conducted statistical analysis of their mental health status. These findings effectively meet the practical application needs for monitoring and warning systems in student mental health.