Multi-source music knowledge graph construction and prosodic evolution trajectory prediction via DRL-VAE collaborative learning.

The proliferation of heterogeneous music data across diverse modalities presents significant challenges for unified music information retrieval and analysis. This paper proposes a novel framework integrating deep reinforcement learning (DRL) with variational autoencoders (VAE) for multi-source music knowledge graph construction and prosodic evolution trajectory prediction. We design a hierarchical ontological schema accommodating acoustic, symbolic, and semantic modalities, enabling systematic entity alignment across disparate data sources. The DRL-VAE collaborative mechanism leverages variational encoding for structured latent representations while employing reinforcement learning agents to optimize sequential knowledge graph completion decisions. A prosodic trajectory prediction algorithm is developed that synthesizes generative modeling with long-horizon planning, contextualized through graph neural network layers propagating structural information from the constructed knowledge base. Experimental evaluation on large-scale public datasets demonstrates that the proposed approach achieves an MRR of 0.456 on link prediction tasks, representing a 17.8% improvement over baseline methods, while trajectory prediction attains an F1-score of 0.867 with superior generalization across musical styles. The framework proposes an integrated methodology bridging music knowledge representation and computational prosodic analysis.