Construction of College Students’ Mental Health Assessment and Art Therapy System Aided by the Internet of Things and Big Data

Abstract

In order to improve the effect of college students’ mental health assessment, this paper combines the Internet of Things and big data technology to build a college student’s mental health assessment system and analyzes and verifies the clustering effectiveness indicators by using FCM, GK, and GG clustering algorithms. Moreover, this paper explores the basic concepts and processes of FCM algorithm, GK algorithm, and GG algorithm and expounds the connection between the three algorithms. In addition, this paper uses four datasets to conduct clustering experiments and compares the CS indicator with several other indicators. The experimental results demonstrate the effectiveness of the CS indicator. The simulation study shows that the student mental health assessment system based on the Internet of Things and big data technology proposed in this paper can play a certain role in student mental health and art therapy, and it also verifies that art therapy plays a certain role in student psychotherapy.