## 6 Conclusions  
This study comprehensively utilized machine learning and SHAP interpretability models to systematically analyze the weak links in the mechanized mining system of underground metal mines, leading to the following key conclusions:  
1. The XGBoost algorithm was employed to establish a regression relationship between multiple factors and the production capacity of the stope (PC), with parameters optimized using the MPA. The results demonstrated that the MPA achieved an optimal performance with a swarm size of 60. Under these conditions, the MPA-XGBoost coupling model exhibited an exceptional predictive performance for PC, achieving an R\({}^{2}\) value of 0.958, VAF of 95.981%, MAE of 4.844, and RMSE of 7.033. Compared with traditional XGBoost models, PSO-XGBoost models, and common machine learning techniques such as SVM, this model showed significant advantages in regression accuracy, dataset interpretability, and capturing nonlinear relationships, providing a more precise representation of the relationship between PC and various influencing factors.
2. Using the SHAP method, the importance and contribution degree of different stope mining factors to PC were quantified. DE had the most significant overall impact on PC, contributing positively by 35.6%. VT, EMT, DOMQ, and ORE had similar contributions, accounting for 14.2%, 13.9%, 13.7%, and 13.1%, respectively. Among these, VT and EMT exerted negative impacts, while DOMQ and ORE had positive effects. ST and CBE had relatively smaller contributions, at 3.9% and 5.5%, respectively.
3. By constructing SHAP dependency plots, the influence patterns of key factors on PC were clearly illustrated. As DE increased, PC significantly improved, particularly when DE exceeded 225, where the effect became more pronounced. For VT and EMT, PC decreased gradually below a specific threshold (the tipping point) but dropped sharply above it. This indicates that increasing DE and reducing VT and EMT can significantly enhance stope PC.
4. This study effectively identified the weak links in the mechanized mining system of underground metal mines through advanced modeling, quantified the impact of various factors on mining efficiency, and provided a scientific basis for optimizing mining processes. Mining production managers can leverage these findings to improve specific production processes, such as enhancing drilling efficiency, optimizing ventilation systems, and refining equipment maintenance procedures, thereby boosting overall mine productivity and ensuring safe operations.  
**Author Contributions:** Conceptualization, K.Z.; Methodology, C.Y. and J.L.; Data curation, C.Y.; Writing--original draft, C.Y.; Writing--review & editing, J.L.; Supervision, K.Z.; Funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was funded by the Changsha Science and Technology Project (Grant Nos. kq2402209) and Guangxi Key Research and Development Program (Grant Nos. 2023 AB01130) and the Fundamental Research Funds for the Central Universities of Central South University (Grant Nos. 1053320220742).  
**Data Availability Statement:** The datasets generated during and analyzed during the current study are available from the corresponding author on reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.