In the medical field, retrieval-augmented generation (RAG) has been proposed to mitigate hallucinations in large language model (LLM) and enhance the interpretability and controllability. However, existing techniques are faced with poor recall of low-frequency entities and difficulties in processing ambiguous, verbose, or polysemous queries. To this end, this study proposes an iterative hybrid retrieval-augmented generation (IHRAG) approach for LLM to improve the intention parsing ability of complex queries and enhance the model’s performance in knowledge mining capabilities for making LLMs generate more accurate responses. IHRAG employs a dynamic routing mechanism to synergistically leverage the semantic generalization capability of vector retrieval andthe structured reasoning capacity of knowledge graphs. By combining a medical ontology-driven query decomposition algorithm, complex clinical questionsare broken down into retrievable atomic sub-questions. Furthermore, a knowledge gap-aware neuro-symbolic expansion model and a “retrieve-verify-iterate” closed-loop optimization mechanism are introduced to establish a progressive discovery process that advances from surface-level information extraction to deep knowledge mining. Experiments demonstrate that IHRAG significantly enhances the performance of base models of various scales such as Qwen and DeepSeek, achieving an improvement in the accuracy of up to 11.12 percentage points and a 17% increase in the high-quality response rate.