Research on the Path of Empowering the Digital Inheritance of Red Music Culture and Enhancing Social Cohesion with Convolutional Neural Networks
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Abstract
Under the dual drive of cultural digitization strategy and innovation in red-culture inheritance, red music culture, as a medium of revolutionary history and national unity, faces practical bottlenecks such as discontinuity of inheritance subjects, solidified dissemination forms, insufficient reach to young audiences, and weakened social-emotional connection. Convolutional neural networks (CNNs), with local receptive fields, weight sharing, and hierarchical feature extraction, provide efficient technical solutions for full-lifecycle digital governance of red music culture. This article reviews research progress in CNN-based digital protection of cultural heritage and identifies the practical pain points of red music inheritance. It constructs a full-chain CNN empowerment system covering collection, restoration, recognition, creation, dissemination, and evaluation, and designs five core application scenarios: intelligent restoration of red music images, scores, and audio; intelligent classification and retrieval of tracks; style transfer and creative revitalization; immersive narrative communication; and quantitative evaluation of social and emotional effects. By extending digital audio and image signal processing to cultural heritage scenarios, the framework supports resource standardization, precise dissemination, and youth-oriented communication. The study provides a replicable technical path for improving the digital efficiency, dissemination effectiveness, and social cohesion value of red music culture.
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