Analysis and Countermeasures of Psychological Characteristics in College Students’ Psychological Education Based on Artificial Intelligence

Abstract

ABSTRACT It is true that with society entering a new era, the pressure of competition for employment of college students is increasing, and society has higher requirements for the quality of high-tech talents, which can result in mental health issues for college students. Therefore, focusing on psychological education for college students and improving their psychological quality is essential. The analysis and countermeasure research on psychological characteristics has become crucial in this context. To address this issue, this work proposes an Improved Particle Swarm Optimization (IPSO) Deep Belief Network (DBN) method to analyze the psychological characteristics of college students. The proposed method first uses DBN to analyze the psychological characteristics of college students. Secondly, the learning factors and inertia weights in PSO are dynamically adjusted to construct IPSO to address the problem of slow overall convergence speed caused by the initial randomization of weights in the DBN training process. IPSO is then combined with DBN to adjust the initial weights for DBN training. This work also proposes a series of countermeasures from different aspects to strengthen mental health education for college students. The validity of IPSO-DBN for college students’ psychological characteristics analysis is verified through comprehensive and systematic experiments, and the feasibility of college students’ mental health education countermeasures is confirmed via comparison. Overall, this work provides an innovative and effective approach for analyzing college students’ psychological characteristics and developing countermeasures to improve their mental health. It shows the potential of combining artificial intelligence with psychological education and highlights the importance of strengthening psychological health education for college students.