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) is proposed.First, entities and relations are extracted from the original documents to construct a basic knowledge graph. Then, summary nodes are generated for closely related entity groups, forming a hierarchical summary-enhanced knowledge graph. During query processing, complex questions are decomposed into sub-question sequences through dynamic question decomposition. Multi-hop neighbor expansion retrieval is performed using the summary-enhanced knowledge graph. Finally, the results from multiple reasoning paths are 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.