Industry 4.0 of the 21st century has given birth to smart mines. The multidisciplinary datasets of smart mines-such as geology, exploration, mining, geometallurgy, environment and survey/map datasets-constitute big data of mines, and they play an important role for the rapid advancements of geoscience in areas of geoscience digitization and application of information/Al technology in geoscience. Taking the Shangfanggou Mo (Fe) mine, a 5G+ smart mine, in Henan Province as an example, using big data of mines, this paper carried out geoscience information mining to highlight emerging engineering research with integrated multidisciplinary approach. Innovative results and geological knowledge discoveries from this study are summarized as follows: (1) According to theories on porphyry-associated skarns and mineralogical approach to minera resources prospecting, using borehole datasets and large-scale open-pit mapping and microscopic identification analysis, a 3D temporo-spatial model of the identified key minerals and predicted minerals in the study area was established, and a NE trending ore-bearing fault section and a penetration-type ore-bearing section were discovered. (2) Using UAV remote sensing and ground hyperspectral short-wave/long-wave infrared techniques, more than 20 types of key altered minerals in the study area were delineated, and a 3D multi-parameter mineral model was constructed. (3) Using geochemical techniques such as XRF and in-situ microscopy, a rock dataset with matching hyperspectral interpretation was established, and a dual-matrix mapping software for useful/harmful elements of rocks/ores in the study area was developed. In addition, mathematical modeling combining traditional geostatistics (gauss simulation, kriging interpolation) with machine learning (deep learning) was realized, and the composition of ore blends used between March-April 2021 was identified and the cause of the resulting low recovery rate was clarified. (4) Based on process mineralogy practice in the study area, multi-stage, multi-type mineral processing datasets (>1800 data on quarterly/monthly/daily processing of rock powder, mud powder, concentrate, tailings, etc.) were used to develop rock/mineral powder testing techniques and analysis methods, and the types of refractory ores and harmful minerals in the Shangfanggou Mo mine were identified. The multivariate, multi-type datasets of mines have the “5V” (volume, variety, velocity, veracity, value) characteristics of big data. The accurate management control of dynamic correlation measurement/analysis and rapid/efficient evaluation of big data of mines is conducive to intelligent mining decision-making and improvement of economic benefit (recovery rate). Among them, high-precision multi-parameter 3D modeling can be applied not only to deep mining of geological, structural, alteration and mineralization information models of rocks/ores as well as reserve/resources verification, but also to facilitating 4D control on real-time mining of fourth generation industrial 5G+ mines, such as 3D visualization of geological and mineral resources prediction/evaluation/storage expansion, virtual simulation of “year-quarter-month-day” dynamic ore blending and mining, and real-time digital twin for mine beneficiation. The research results provide a reference for in-depth geoscience research on mineral exploration and mineral resources assessment in smart mines.