Data mining-based innovative model for mental health of college students using IoT and big data analysis.

The mental health of college students not only affects their personal development but also impacts the environment of the campus. Moreover, it affects the harmony of the family and stability of the society. This phenomenon has attracted great attention from the society. New technological paradigms such as the Internet of Things (IoT) and data mining offer new opportunities to detect, assess, and care for the mental health of students early. This paper explains the psychological problems of college students and analyzes the application of IoT and data mining in the mental health of students. The contents of mental health in colleges are investigated, and big data techniques are employed to build an innovative model for students. First, an online psychological consulting platform is designed followed by online evaluation standards and online/offline teaching fusion courses to understand mental health. Next, a neural network algorithm is developed and trained on the datasets to accurately classify the contents of students' mental health, and the corresponding results are obtained. The model is evaluated in terms of accuracy. Results show that the proposed innovative model achieves better results with 88% accuracy. Through comparative analysis, it is demonstrated that the use of IoT and big data technology can provide technical support for the innovation of college student's mental health and education and can solve more problems efficiently. [ABSTRACT FROM AUTHOR]