AI-driven Innovation in the Music Industry: Mechanism Design and Theoretical Framework - Taking the Lingnan Culture "Double Innovation" Project as a Scenario

Abstract

In alignment with regional cultural empowerment strategies, this study proposes a systematic theoretical framework and application model termed 'AI+Music+N' to accelerate cultural-technological integration. Centered on artificial intelligence, the framework utilizes data governance, algorithmic design, and immersive technology to reshape the music industry value chain, extending its impact across cultural tourism, education, healthcare, sports, and public services. Building on existing literature, the research elucidates the intrinsic mechanisms of the data element multiplier effect in music consumption markets, including network externalities and contextual expansion. It formally analyzes how AI enhances matching efficiency, contextual adaptation, and copyright incentives through a multilateral platform model. The study constructs a tripartite framework integrating technology, organization, and application scenarios, proposing exemplary demonstration projects rooted in Lingnan culture. Empirical evaluation designs, including multi-source data integration and A/B testing, are developed alongside governance frameworks addressing copyright adherence, data security, and algorithmic ethics. Findings demonstrate that when AI enhances recommendation accuracy and contextual relevance, platforms experience significant improvements in overall welfare, premium content supply, and the digital revitalization of intangible cultural heritage. Integrated copyright smart contracts and differentiated revenue-sharing mechanisms inherently incentivize creation-production-supply-use synergy, facilitating new productivity models. Ultimately, this study contributes a verifiable mechanism model and policy toolkit, providing replicable theoretical frameworks and practical pathways for innovative cultural industry development and technological integration.