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| Citation: | Wu Hailu, Dou Lei, Yu Pengpeng, Zhu Shibo, Yu Deyan, 2026. Construction of a Knowledge Graph for Rare Metal Deposits in South China Based on Large Language Models. Earth Science, 51(3): 996-1008. doi: 10.3799/dqkx.2026.051
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The new generation of artificial intelligence technologies, represented by Large Language Models (LLMs), provides new opportunities for the structured representation and intelligent reasoning of geological knowledge. To address the challenges posed by the complexity of geoscientific knowledge systems, as well as the semantic fragmentation, limited reusability, and poor visualizability of unstructured texts, this study proposes a unified strategy for constructing a knowledge graph that integrates deposit genesis and prospecting indicators, taking rare metal deposits in South China as a study object. Based on the DeepSeek R1-32B large language model and prompt engineering, a knowledge graph covering key rare metal elements such as Li, Be, Nb, and Ta, is automatically extracted and constructed. The knowledge graph construction and its extensibility analysis indicate that rare metal mineralization in South China is closely associated with Indosinian and Yanshanian magmatic activities, characterized by significant high-degree fractionation and magmatic-hydrothermal processes. Rare metal elements exhibit a combinatorial anomaly of Li-Be-Nb-Ta-W-Sn. It is concluded that the knowledge graph constructed using LLMs reveals the multi-stage metallogenic mechanisms of rare metals in South China, clarifies the intrinsic relationships among geochemical anomalies, structural controls, and alteration zoning of rare metal deposits, and provides an intelligent research framework for the exploration of rare metals in South China and adjacent regions.
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