Abstract: Based on the relevant papers collected in CNKI database from 2012 to 2022, the paper makes a scientometric analysis of the research in the field of geological big data in China, in order to provide reference for the relevant management departments and the general peers to understand the development trend and evolution of domestic geological big data research in the past decade. The results show that geological big data research has received more attention after 2015, and is currently in a stable and high-yield period. There are three research institution networks with the largest number of publications and the greatest influence, the core of which are the Development Research Center of China Geological Survey, China University of Geosciences（Beijing） and China University of Geosciences（Wuhan）. Co-occurrence analysis shows that 1 large and 4 small groups of authors have been formed, but the core group of authors in this field has not yet been formed. The main research hotspots are geological big data storage and management, geological big data mining process and algorithm research, geological big data application. Among them, the geological big data mining methods represented by machine learning and deep learning have the highest research heat and the most achievements. The hot areas of geological big data application exploration mainly include intelligent geological survey, mineral resource prediction, geochemistry, geological disaster warning, and intelligent resource mining. In the first stage（2013-2015）, the research hotspots mainly include geological disaster warning and mineral prediction. The second stage（2016-2018） mainly focuses on cloud computing, cloud platform and other related research and the development and utilization of geological data; The third stage（from 2019 to now） takes artificial intelligence and knowledge map as research hotspots, and enters the exploration stage of intelligent geology. Artificial intelligence algorithm, geological knowledge map, and the combination of big data-driven and knowledge-driven are the current research frontiers in the field of geological big data.