‌Enhancing Ancient Ceramic Knowledge Services: A Question Answering System Using ‌Fine-Tuned Models and GraphRAG

‌To address the challenges of extensive domain expertise and deficient semantic comprehension in the digital preservation of ancient ceramics, this paper proposes a knowledge question answering (QA) system integrating Low-Rank Adaptation (LoRA) fine-tuning and Graph Retrieval-Augmented Generation (GraphRAG). First, textual information of ceramic images is generated using the GLM-4V-9B model. These texts are then enriched with domain literature to produce ancient ceramic QA pairs via ERNIE 4.0 Turbo, culminating in a high-quality dataset of 2143 curated question–answer groups after manual refinement. Second, LoRA fine-tuning was employed on the Qwen2.5-7B-Instruct foundation model, significantly enhancing its question-answering proficiency specifically for the ancient ceramics domain. Finally, the GraphRAG framework is integrated, combining the fine-tuned large language model with knowledge graph path analysis to augment multi-hop reasoning capabilities for complex queries. Experimental results demonstrate performance improvements of 24.08% in ROUGE-1, 34.75% in ROUGE-2, 29.78% in ROUGE-L, and 4.52% in BERTScore_F1 over the baseline model. This evidence shows that the synergistic implementation of LoRA fine-tuning and GraphRAG delivers significant performance enhancements for ceramic knowledge systems, establishing a replicable technical framework for intelligent cultural heritage knowledge services.