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<p>In the context of the digital transformation of ideological and political education (IPE) in the new era, this study explores the interdisciplinary integration of red music and intelligent recommendation technologies. An intelligent deep learning model is developed to recommend IPE resources enhanced with red music, addressing challenges such as the low precision of traditional IPE resource delivery and limited emotional engagement. The model employs multimodal feature extraction techniques to fuse the emotional content of red music—captured via short-time Fourier transform and time-frequency attention mechanisms—with lyrical semantics. Learner profiles are constructed using dynamic cognitive diagnosis combined with Transformer-based temporal sequence modeling. Based on these profiles, precise resource recommendations are generated through heterogeneous information networks and hierarchical reinforcement learning. Experimental results indicate that the proposed model significantly outperforms the comparative methods across several metrics. Recommendation accuracy improved by 23%–35%. Educational relevance increased by up to 29%, and emotional resonance grew by 27%. Pilot tests in college I&amp;P courses demonstrate that the model effectively enhances student engagement. Overall, this study offers a technology- and education-driven paradigm for the digital inheritance of red culture. Future work could expand the model to incorporate additional multimodal data and explore cross-cultural applications, further promoting intelligent and personalized development in IPE.</p>

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Intelligent deep learning model for recommending ideological and political music education resources

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月17日

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  • Lifang Zhang

摘要

In the context of the digital transformation of ideological and political education (IPE) in the new era, this study explores the interdisciplinary integration of red music and intelligent recommendation technologies. An intelligent deep learning model is developed to recommend IPE resources enhanced with red music, addressing challenges such as the low precision of traditional IPE resource delivery and limited emotional engagement. The model employs multimodal feature extraction techniques to fuse the emotional content of red music—captured via short-time Fourier transform and time-frequency attention mechanisms—with lyrical semantics. Learner profiles are constructed using dynamic cognitive diagnosis combined with Transformer-based temporal sequence modeling. Based on these profiles, precise resource recommendations are generated through heterogeneous information networks and hierarchical reinforcement learning. Experimental results indicate that the proposed model significantly outperforms the comparative methods across several metrics. Recommendation accuracy improved by 23%–35%. Educational relevance increased by up to 29%, and emotional resonance grew by 27%. Pilot tests in college I&P courses demonstrate that the model effectively enhances student engagement. Overall, this study offers a technology- and education-driven paradigm for the digital inheritance of red culture. Future work could expand the model to incorporate additional multimodal data and explore cross-cultural applications, further promoting intelligent and personalized development in IPE.