This paper proposes a large language model (LLM) approach that integrates graph-structured information for knowledge reasoning in tobacco pest and disease control. Built upon the GraphRAG framework, the proposed method enhances knowledge retrieval and reasoning by explicitly incorporating structured information from a domain-specific knowledge graph. Specifically, LLMs are first leveraged to assist in the construction of a tobacco pest and disease knowledge graph, which organizes key entities such as diseases, symptoms, control methods, and their relationships. Based on this graph, relevant knowledge is retrieved and integrated into the reasoning process to support accurate answer generation. The Transformer architecture is adopted as the core inference model, while a graph neural network (GNN) is employed to learn expressive node representations that capture both local and global relational information within the knowledge graph. A ChatGLM-based model serves as the backbone LLM and is fine-tuned using LoRA to achieve parameter-efficient adaptation. Extensive experimental results demonstrate that the proposed approach consistently outperforms baseline methods across multiple evaluation metrics, significantly improving both the accuracy and depth of reasoning, particularly in complex multi-hop and comparative reasoning scenarios.

翻译：本文提出了一种融合图结构信息的大语言模型方法，用于烟草病虫害防治领域的知识推理。该方法基于GraphRAG框架，通过显式整合领域特定知识图谱中的结构化信息，增强了知识检索与推理能力。具体而言，首先利用大语言模型辅助构建烟草病虫害知识图谱，该图谱组织整理了病害、症状、防治方法等关键实体及其相互关系。基于此图谱，检索相关知识并将其整合到推理过程中，以支持准确答案的生成。采用Transformer架构作为核心推理模型，同时利用图神经网络学习知识图谱中节点的表征，以捕捉局部与全局关系信息。以ChatGLM模型作为骨干大语言模型，并使用LoRA进行微调，以实现参数高效的适配。大量实验结果表明，所提方法在多项评估指标上均持续优于基线方法，显著提升了推理的准确性与深度，尤其在复杂的多跳推理与比较推理场景中表现突出。