【摘要】 为揭示我国智能矿山研究的知识结构、热点迁移及其驱动机制，选取10种矿业工程技术高影响力期刊为数据源，采集2015年1月—2025年12月24 960篇论文元数据，构建基于大语言模型（LLM）的语义筛选与主题分类流程，筛选出智能矿山相关文献4 584篇。通过文献分析发现，智能矿山领域研究分为3个阶段：探索阶段、起步阶段和建设阶段。基于关键词与热点演化分析发现，研究从物联网、安全监测监控等领域逐步迁移至人工智能、智能开采、数字孪生等方向。最后从政策引导、安全需求、效益驱动、技术驱动等多元因素解释我国智能矿山研究“自上而下”驱动模式的形成机制。更多还原

【Abstract】 To reveal the knowledge structure, hotspot migration, and driving mechanisms of intelligent mining research in China, this study selected 10 high-impact journals in mining engineering and technology as data sources, collecting 24 960 paper metadata records published from January 2015 to December 2025. A semantic screening and thematic classification workflow based on a large language model(LLM) was developed, identifying 4 584 intelligent mining-related publications. Bibliometric analysis reveals that research in the intelligent mining field can be divided into three stages: the exploratory stage, the initial development stage, and the in-depth construction stage. Keyword and hotspot evolution analysis indicates that research focus has progressively shifted from domains such as the Internet of Things(IoT) and safety monitoring to areas including artificial intelligence, intelligent mining, and digital twin technologies. Finally, the formation mechanism of China′s ′top-down′ driving model for intelligent mining research is explained from multiple perspectives, including policy guidance, safety demands, economic benefits, and technological innovation.更多还原