Design of psychological early warning cloud platform for college students based on artificial intelligence technology

This paper designs a psychological early warning cloud platform for college students based on artificial intelligence technology, aiming to realize the accurate evaluation and effective intervention of the mental health state of college students through multi-source data acquisition, advanced deep learning algorithm, real-time data processing and dynamic early warning mechanism. The platform uses Internet of Things devices, mobile applications and online questionnaire system for data collection, data processing and mental state assessment through algorithms such as deep neural network, support vector machine and long and short-term memory network, and provides timely psychological support through multi-channel information push and personalized intervention programs. Functional evaluation indicators show that the platform performs well in data accuracy, response speed, notification timeliness and user satisfaction, providing strong technical support for mental health services in colleges and universities.