Research on the Path of Empowering the Digital Inheritance of Red Music Culture and Enhancing Social Cohesion with Convolutional Neural Networks

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.