Theoretical Analysis of Graph Diffusion Models Applied to Intelligent Question Answering Systems

Intelligent question answering systems often face limitations in semantic understanding depth and knowledge retrieval efficiency when handling complex natural language queries. To address these challenges, this paper proposes an enhanced intelligent QA system framework based on graph diffusion modeling. We conduct rigorous mathematical modeling of the graph diffusion module, employing the Random Walk with Restart (RWR) algorithm, and formally prove the convergence of the diffusion process. Through these mathematical modeling and optimization strategies, the system achieves improved capability in capturing latent semantics of user queries and generating more accurate responses. Our research provides theoretical support and practical guidance for enhancing the performance of intelligent question answering systems.