The outdated methods of storage and preservation currently in use pose a significant risk of traditional music cultural resources being lost, necessitating an effective form of resource information protection. This paper explores the integration of traditional music culture with the digital age, taking into account the unique characteristics of traditional music. For data processing, a Bayesian classifier is employed to categorize traditional music data types. Based on this, a semantic proactive service workflow centered on “resource collection-resource analysis and organization-resource publication” is designed, leading to the development of a proactive service architecture. Subsequently, using an information grid model, user needs and resource content are grid-modeled to comprehensively establish a semantic model for traditional music culture. A collaborative filtering recommendation algorithm is introduced, improving the Apriori algorithm to address its issues of data sparsity and cold start problems, thereby enhancing the accuracy of recommendation results. Combining the traditional music culture semantic model with the improved recommendation algorithm, a preliminary digital display system for traditional music culture was established, tested, and evaluated for performance. The designed system model demonstrated significantly superior recommendation accuracy (HR@10 > 0.5, NDCG@10 > 0.5) and average recommendation error (0.75) compared to similar models across various experimental environments.