## 5 Conclusions  
This study, based on the geoscience big data of the Yanshan Iron Mine, employed machine learning (deep learning) methods to carry out a series of smart-mine-related technological attempts, including sample elemental correlation analysis, hyperspectral analysis of rocks and minerals, magnetite intelligent identification, and three-dimensional mineralization prediction, successfully demonstrating the application of machine learning techniques in smart mine construction and providing valuable insights and practical methods for deep mineral exploration, intelligent mineral identification, and real-time monitoring in mining areas. 1. Self-organizing map (SOM) clustering analysis of X-ray fluorescence (XRF) elemental data from 218 ore samples revealed that the samples from the study area could be divided into two clusters. In the first cluster, elements such as Mg, Al, Si, S, K, Ca, Mn, and Fe exhibited a very strong positive correlation, while Ti, Co, Zn, and Sr showed a weak positive correlation with Fe. Elements such as V, Cr, Ni, Cu, Y, Zr, Nb, Mo, and Pb displayed a negative correlation with Fe. The second cluster consisted of Rb and Th, which showed a positive correlation. Further analysis using TSG shortwave and thermal infrared hyperspectral rock data identified the main mineral types in the mining area, including chlorite, rhodochrosite, dolomite, amphibole, biotite, montmorillonite, quartz, and feldspar. Combined with the XRF results, it was concluded that the region is characterized by significant hydrothermal alteration, primarily chloritization and carbonation, closely related to mineralization. Mg, Al, Si, and Ca were identified as important indicator elements for further deep exploration. 2. Based on high-precision drone multispectral data and XRF sample grade data, an ore grade-spectrum correlation model was constructed using Random Forests, Support Vector Machine algorithms, and SMOTE algorithms. After evaluating multiple performance metrics, the RF23 model was selected as the optimal model for real-time prediction of the surface total iron grade in the mining area, with an ore body identification accuracy of 0.79. The model was applied to centimeter-level drone images, achieving high-precision intelligent identification of magnetite in the mining area. The drone multispectral image prediction clearly delineated the boundaries of rock minerals, aligning well with the grade distribution of measured samples, especially in the stope and blasted rock powder areas. Combined with LiDAR image elevation data,  
Figure 24: Comparative results of high-mineralization zones in BPUL predictive model with (**a**) chloritization and (**b**) mignatization. real-time monitoring of the three-dimensional surface mineralization information of the mining area was successfully realized, providing significant support for improving ore recovery rates and real-time detection in the mining area, demonstrating great practical application value. * A three-dimensional geological model was constructed to perform three-dimensional mineral resource potential evaluation (MPM). The results show that the BPUL algorithm can be effectively applied to deep mineral exploration prediction in the Yanshan Iron Mine. The predicted results closely aligned with the spatial location of high-grade mineralization zones. The P-V diagram analysis helped identify the high-mineralization areas at the scale of the mining area, pinpointing two potential exploration targets in the deep and northwest regions. SHAP values and the morphological features of different three-dimensional geological models indicated that chloritization, mixed rock alteration, and magnetic anomalies have significant contributions to ore body enrichment, while faults have some control over the morphological distribution of the Yanshan Iron Mine, but their contribution to the formation of high-grade ore bodies is relatively small. Conceptualization, Y.C. and G.W. ; Methodology, Y.C. and N.M.; Validation, Y.C. and N.M.; Formal analysis, Y.C.