Abstract: Mineral resources, as an important foundation for national economic development, are characterized by their non-renewable and limited nature, which necessitates efficient and scientific exploration and utilization. With the accelerated industrialization and urbanization in China, the demand for mineral resources is increasing, while traditional exploration methods can no longer meet these needs. At the same time, the advancement of the new round of mineral exploration breakthrough strategic action and the need to ensure national energy security have raised higher requirements for software in the field of the prospecting. Against this backdrop, this paper summarizes the application of big data digging technology in geological mineral exploration and the current research status of related software systems. Using the “MinExplorer” package as an example, some details are provided. The software aims to offer efficient and accurate tools for geological workers by integrating artificial intelligence algorithms, enhancing exploration efficiency and accuracy, and promoting the intelligent development of mineral exploration in China. The software adopts a modular design, consisting of five functional modules: spatial data management, computer-aided design, three-dimensional (3D) geological modeling, resource estimating, and three-dimensional mineral prospectivity mapping (MPM). The spatial data management module supports multi-source data processing and visualization; the 2D geological mapping can generate standard geological maps for estimation and prospectivity; the 3D geological modeling module can model and display complex geological structures and bodies; the resource estimating module integrates multiple methods to scientifically evaluate resource volumes; and the three-dimensional MPM provides tools for predicting mineral resources in three-dimensional space. These five modules work collaboratively to improve the efficiency of geological workers and the accuracy of mineral resource evaluation, providing strong support for exploration efforts. During development, the “MinExplorer” software fully leverages the advantages of existing domestic and international software and achieves key breakthroughs by integrating artificial intelligence technology. Especially, the intelligent prediction module introduces Convolutional Neural Networks (CNN) in prospectivity mapping both 2D and 3D, which can mine nonlinear features from multi-source geological data to provide efficient and accurate resource potential predictions. With advanced visualization technology, the software supports real-time interaction and dynamic updates, providing users with a more intuitive and convenient operating experience. In practical applications, the “MinExplorer” software has been widely used in several regions across the country. For example, in the Haoyaoerhudong Gold Mine in Inner Mongolia, the software is used to construct more than 30 three-dimensional models of ore bodies, faults, altered minerals, geochemical elements, and more, and delineate multiple deep exploration target areas and peripheral mineral exploration zones. Drill verification has revealed that the gold ore body exposed by single-hole drilling has a cumulative true thickness of 40-51 meters, with an average grade of 0.53-0.82 g/t, demonstrating significant exploration success. In the future, with continuous technological progress, geological mineral exploration software systems will continue to develop in an intelligent direction, further improving performance and providing more efficient and accurate tools for geological workers. This will drive the intelligent transformation of the geological exploration field, laying a more solid foundation for ensuring national energy security and achieving the goals of the mineral exploration breakthrough strategy.