With the accelerated pace of society and the increasing pressure of competition, the issue of mental health has received increasing attention. Especially in the field of education, students’ mental health status directly affects their student outcomes and overall development. The aim of this study is to design a mental health status monitoring system based on large-scale data streaming computation, to realize dynamic real-time monitoring of individual mental health through multi-source data acquisition and efficient algorithm processing, and to explore its application in educational scenarios. Sliding window algorithm and Hidden Markov Model are used to analyze and process the collected multi-source data such as physiological signals, and the experimental results show that the system is able to significantly test the difference between people with high and low scores on psychological test scales in the monitoring of mental health status, and it can provide educators with valuable decision-making support and help students’ mental health education and intervention.