Optimization Research on Integrating Mental Health Into Ideological and Political Theory Courses Through Deep Learning Network Applications

Abstract

This study aims to evaluate the effectiveness of incorporating mental health education into ideological and political theory classrooms using speech emotion recognition technology from the field of human-computer interaction. Mental health education is essential for students' well-being, and this study establishes a theoretical framework for its integration into the curriculum. Utilizing recurrent neural networks (RNNs) augmented with attention mechanisms, the research demonstrates the model's proficiency in identifying subtle emotional cues within speech data. The model achieves an average recognition accuracy of 87.21% on the RAVDESS speech emotion corpus, showing particular strengths in detecting emotions like happiness and boredom. The findings suggest that this technology can be effectively employed to gauge student satisfaction with different teaching methodologies post-integration, offering valuable insights for refining educational practices in ideological and political theory classes through improved mental health education initiatives.