A Structure Tree-Based Multi-Document Retrieval-Augmented Generation Method for Civil Aircraft Manufacturing Processes

Traditional Retrieval-Augmented Generation (RAG) methods face two major challenges when processing specialized technical documents in fields such as civil aircraft manufacturing processes due to their fixed segmentation strategies: first, loss of contextual information caused by disrupting the inherent chapter structure of documents; second, lack of cross-document associations resulting from treating documents as isolated information silos. To systematically address these issues, this paper proposes a novel Hyperscale Cross-Document Retrieval Framework (HCRF). This framework aims to fully leverage the intrinsic structure and semantic relationships within documents by constructing document structure trees, performing cross-document topic clustering and abstract generation, and implementing hierarchical retrieval and hybrid ranking strategies. Experiments on a self-built civil aircraft manufacturing Q&A dataset demonstrate that HCRF significantly outperforms existing baseline models in both retrieval accuracy and answer generation quality, validating the framework’s unique advantages in processing structured technical documents.