Design and Development of a Digital Platform for Traditional Chinese Music Culture based on Cloud Computing Technology

The preservation and promotion of Traditional Chinese Music (TCM) are increasingly threatened in today’s digital age by dispersed resources, absence of smart categorization, and restricted accessibility. Current systems focus mainly on static websites or manual categorization methods, providing limited interactivity and poor scalability. Such conventional approaches are inappropriate for providing real-time access, individualized recommendations, and smart content structuring, thereby compromising user interaction and dissemination of culture. To meet these challenges, this research introduces a cloud computing–based digital platform that incorporates a hybrid CNN–KNN algorithm. The CNN block identifies deep audio features from conventional music recordings, and the KNN block facilitates accurate classification and customized music recommendation. The platform is hosted on a very scalable cloud platform, making use of technologies like cloud storage (AWS S3, Alibaba OSS) and containerized deployment (Docker and Kubernetes) to achieve real-time access, high availability, and effective resource utilization. In contrast to keyword-based and rule-based systems, the model presented in this paper showed significant improvements in accuracy, speed, and user satisfaction, with a 97% classification accuracy in testing. Not only does the platform facilitate greater access to traditional Chinese music, it also serves as an intelligent archival and learning tool for global cultural preservation.