Currently, student mental health issues are becoming increasingly prominent and have become a major challenge that families, society, and schools must address urgently. Peer mental health counseling conducted on social media platforms offers distinct advantages. This study explores emotional communication strategies in peer mental health counseling and proposes a self-regulation scheme based on peer counseling. This scheme is implemented using the Kernighan-Liu community mining algorithm and a social network analysis-based information recommendation algorithm. Based on this framework, a comparative experiment was conducted with students from a selected school to investigate the effectiveness of the peer counseling self-regulation method. The information recommendation algorithm proposed in this paper demonstrates high detection effectiveness and efficiency, with a detection rate of 92.61% and a runtime of 0–11.71 seconds. After experiencing the methods proposed in this paper, students’ peer counseling competition scores, peer counseling satisfaction, and willingness to seek help significantly improved at the 1% level, increasing by 39.62%, 111.18%, and 73.14%, respectively. The number of school crises, students’ SCL-90 data, and peer counseling occupational burnout levels decreased by 45%, 7.53%, and 20.52%, respectively, validating the promotional effect of peer counseling self-regulation methods on the emotional transmission of peer counseling.
