Traditional retrieval-augmented generation (RAG) methods exhibit significant limitations in handling complex queries requiring multi-hop reasoning and global understanding, such as cross-chapter question answering in educational contexts. To address these issues, a graph-enhanced retrieval-augmented generation mechanism for multi-hop QA (GRAG-MH) was proposed. Firstly, entities and relations were extracted from the original documents to construct a basic knowledge graph. Then, summary nodes were generated for closely related entity groups, forming a hierarchical summary-enhanced knowledge graph. During query processing, complex questions were decomposed into sub-question sequences through dynamic question decomposition. Multi-hop neighbor expansion retrieval was performed using the summary-enhanced knowledge graph. Finally, the results from multiple reasoning paths were integrated via a self-consistency voting method. Experimental results demonstrate that GRAG-MH achieves F₁ scores of 53.7% and 50.7% on the multi-hop QA benchmark datasets HotpotQA and 2WikiMultihopQA, respectively, outperforming traditional RAG methods by over 50%. By combining interpretable multi-path reasoning with hierarchical knowledge representation, the proposed method significantly improves the accuracy of QA systems.