Research hotspots and trends analysis in smart mining based on bibliometrics

In the context of the global digital transformation of the mining industry, smart mining research has emerged as a critical pathway to achieve high-quality development in China’s mining sector. This paper systematically analyzes the evolutionary trajectory and frontier trends of domestic smart mining research by utilizing 364 core journal articles indexed in the China National Knowledge Infrastructure （CNKI） from 2014 to 2024, with CiteSpace-based visualization analysis. The findings reveal three key insights. The research progression follows a three-phase transition: “foundational exploration, technological breakthroughs, and ecosystem construction”, initially focusing on digital mine theories and information architectures, later advancing intelligent equipment and automation technologies, and currently shifting toward integrated innovations combining 5G, AI, and cloud computing. The research hotspots exhibit a hierarchical structure encompassing “foundational technologies, core applications, and emerging convergences”, where digital twins, 3D geological modeling, and intelligent control systems remain dominant, while emerging topics such as 5G communication, edge computing, and heterogeneous data fusion show accelerated growth. The research trends highlight three paradigm shifts: from single-technology development to integrated innovation, from localized automation to full-process intelligent management, and from production-centric approaches to sustainable ecosystems. A closed-loop “perception-analysis-decision-execution” framework is evolving as the core of smart mining ecosystems. The results demonstrate that smart mining research has entered an interdisciplinary innovation phase, necessitating advancements in intelligent decision-making algorithms, human-machine collaboration, and data governance standardization. Future efforts should prioritize green mining technologies and autonomous intelligent equipment to drive the industry’s transition from “experience-driven” to “data-driven” practices. This paper provides theoretical insights for academia and industry to navigate technological priorities and foster safe, efficient, and ecologically sustainable mining transformation.