Correlation Analysis between Higher Education Level and College Students’ Public Mental Health Driven by AI

Abstract

Generally, there is a certain correlation between the level of higher education and the public mental health of college students. Traditionally, questionnaires and literature research methods are used to analyze the correlation between mental health and higher education, but these methods are always limited by many factors, such as resource conditions, survey paths, theoretical framework, and technical means. In recent years, with the rapid development and application of artificial intelligence technology, a new direction of analyzing the correlation between higher education level and college students’ public mental health has been given. The artificial intelligence method makes the correlation analysis change from subjective to big data algorithm evaluation, which can make up for the shortcomings and inefficiency of traditional methods, truly analyze the degree of correlation, and put forward exact solutions, which is of great significance for further evaluating and monitoring the public mental health of college students in higher education. This study first analyzes different AI algorithms and determines to use convolution neural network and random forest algorithm to establish an AI correlation model. After testing and data analysis, the established model has an accuracy of 87.5% in the determination and analysis of correlation. Compared with support vector machine (SVM) and backpropagation (BP) neural network algorithm, it has a higher recognition accuracy.