Generative Multimodal Learning Driven Intelligent Intervention Model for Adolescent Online Mental Health Education

With the increasing prominence of adolescent mental health problems, the traditional psychological education model has obvious limitations in responding to individual differences, emotional changes and immediate intervention, making it difficult to meet the actual needs of adolescents in the digital context. In recent years, the rapid development of artificial intelligence, especially generative multimodal learning technology, has provided a new breakthrough for mental health education. Aiming at the current problems of single intervention content, slow response, and insufficient personalization, this paper proposes a generative multimodal learning-driven intelligent intervention model, which integrates text, image, and emotion data to achieve dynamic identification of adolescents' psychological state and customized intervention content generation. The model consists of three modules: emotion recognition, generative content construction, and intervention strategy matching, forming a closed loop of identification-generation-feedback. Empirical studies conducted in three middle schools show that the model has significant effects in reducing anxiety, alleviating loneliness, and enhancing student engagement, which validates its potential for application in real-world educational scenarios. The study provides a theoretical foundation and technical path for building a future-oriented intelligent psychoeducation system.