Abstract: Current applications of large language models (LLMs) in geological prospecting face challenges including insufficient domain expertise, data privacy concerns, and model hallucinations. Furthermore, there remains a lack of efficient and rapid knowledge recommendation methods for LLMs in this filed. This study proposes a KG-RAG (knowledge graph-embedded retrieval-augmented generation) framework that automates the extraction and structured representation of geological prospecting knowledge under the constraints of a geological ontology, leveraging large LLMs as tools. It further employs multi-hop retrieval algorithms within the knowledge graph to enhance the depth and breadth of retrieved content, thereby constructing an intelligent question-answering model for geological prospecting. Experimental results demonstrate that KG-RAG achieved scores of 0.807 (Precision), 0.833 (Recall), and 0.819 (F1-score) in knowledge graph construction tasks. Compared to direct knowledge extraction using the baseline LLM (GLM4-9B), KG-RAG delivers improvements of approximately 50% (Precision), 8% (Recall), and 29% (F1-score), respectively. In question-answering tasks, KG-RAG achieved 0.917 (Recall) and 0.88 (Precision), outperforming document vector-embedded retrieval-augmented generation methods by approximately 24% (Recall) and 22% (Precision), respectively. KG-RAG exhibits superior performance in both knowledge graph construction and intelligent question-answering. It effectively collects and represents geological prospecting and mineral exploration knowledge, providing a valuable reference to geologists for the combined application of LLMs and knowledge graphs.