Abstract: This study aims to deepen the understanding of metallogenic regularities and evaluate the prospecting potential of gold deposits in the Jiangnan Orogen. Focusing on the gold deposits within and adjacent to the Jiangnan Orogen, technologies related to the knowledge graph were introduced. A domain knowledge schema was developed using a top-down approach, and the metallogeny-exploration knowledge graph of gold deposits was constructed by integrating deep learning and Large Language Models (LLM). Community detection and Jaccard similarity evaluations were used to analyze the clustering characteristics of the gold deposits.The knowledge schema contains 28 geological entity types and 10 semantic relationship types. The resulting knowledge graph encompasses 60 representative gold deposits in the region, containing 2, 212 geological entities and 5,497 semantic relationships. Community detection successfully extracted key ore-controlling factor combinations and metallogenic regularities, such as "alteration-mineral-strata." Jaccard similarity analysis indicates that the Shuikoushan and Huangjindong gold deposits have high similarities to global large-to-giant deposits, revealing significant prospecting potential in their deep-seated zones and peripheral areas.