Research on the Construction and Application of Earthquake Emergency Information Knowledge Graph Based on Large Language Models

To address the challenges of semantic parsing of multi-source heterogeneous information and the delayed emergency response decisions caused by insufficient relational reasoning capabilities in earthquake emergency management, this study proposes a domain knowledge extraction method for earthquakes based on a large language model combined with a three-level prompt engineering system (TPES-LLM) of “instruction fine-tuning - demand awareness - case matching. ”The method deploys a local large language model using LangChain +QWEN2.5-7B, integrates earthquake domain knowledge through LoRa fine-tuning based on earthquake experts’ classifications and industry standards, and injects seismic knowledge into the model. The multi-head attention mechanism weights are optimized based on the co-occurrence frequency of historical earthquake entities, and demand-aware knowledge identifies key textual features that significantly impact knowledge extraction. Training is performed on 36 known earthquake disaster events to learn the association patterns of entities, relationships, and events hidden within the earthquake case data for case matching. This method significantly enhances the accuracy of entity recognition and the efficiency of relation extraction for complex disaster-related texts. Additionally, a bidirectional graph attention network (Bi-GAT) is designed to enable bidirectional propagation and dynamic aggregation of node features. The path confidence constraint algorithm (PCCA) is used to achieve deep semantic associations of earthquake disaster elements. Based on the Neo4j graph database, an earthquake emergency knowledge graph is constructed. Experimental results from real earthquake events such as the 2022 Luding 6.8-magnitude earthquake, the 2024 Jishishan 6.2-magnitude earthquake, and the 2025 Dingri 6.8-magnitude earthquake show that the accuracy of intelligent Q&A retrieval for the earthquake emergency knowledge graph reaches 89.62%, 87.28%, and 90.23%, respectively. The earthquake emergency knowledge graph based on large language models constructed in this study provides intelligent decision support for earthquake emergencies, with significant application value.