## 5 Discussions  
This study analyzed the weak links of the mechanized mining system in underground metal mines using machine learning and SHAP interpretable models. The research findings hold theoretical and practical significance in multiple aspects but also have certain limitations. Numerous expandable research directions remain for future exploration. From the perspective of model construction and optimization, the MPA-XGBoost coupling model demonstrates superior performance. By optimizing the parameters of the XGBoost model using the MPA, the regression analysis achieves excellent goodness of fit and error control. Compared with traditional XGBoost models and other machine learning techniques such as SVM, the MPA-XGBoost model exhibits higher \(R^{2}\) values, lower \(RMSE\) and \(MAE\), and is closer to the reference point in the Taylor diagram. This indicates its enhanced ability to accurately capture the complex nonlinear relationships between the production capacity of the mining area (PC) and various influencing factors. However, the model's performance is contingent on the selected dataset. The data used in this study were derived from a decade of mining operations at Tongkeng Mine, which introduces regional and temporal limitations. Different mines exhibit significant variations in geological conditions, mining processes, and equipment levels [39]. Future research should broaden the data collection scope to include more diverse mine data, thereby further validating and optimizing the model and enhancing its generalizability. In terms of the analysis of influencing factors, the SHAP method elucidates the contribution and impact patterns of each factor on PC. DE demonstrates the most significant positive influence on PC, whereas VT and EMT exhibit notable negative impacts. This insight provides clear guidance for mine production management, enabling mines to implement targeted measures. For instance, mines can optimize the selection and operation of rock drilling equipment to enhance DE, design efficient ventilation systems to reduce VT, and formulate scientific maintenance plans to minimize EMT, thereby improving overall production efficiency. From the perspective of practical applications, the research findings can directly support the refined management of mines. Based on these conclusions, mines can optimize their mining processes by adopting efficient equipment and enhancing drilling techniques during the rock drilling stage; strategically arranging ventilation facilities to achieve rapid ventilation during the ventilation stage; and establishing an intelligent monitoring system during the equipment maintenance stage to predict equipment failures and conduct proactive maintenance, thereby reducing downtime. However, during the actual promotion and application process, several challenges may arise, such as high technical costs and personnel qualifications. Introducing new equipment and technologies requires substantial capital investment, which some mines may find difficult to afford. Additionally, the adoption of new technologies demands higher professional skills from employees, necessitating enhanced training programs to improve their operational and management capabilities, ensuring that the research findings can be effectively implemented [40]. Future research can further broaden the scope of data samples to encompass mines with diverse geological conditions and mining processes, thereby enhancing the generalizability of the research outcomes. Additionally, future studies can delve deeper into the complex interactions among various factors to provide more comprehensive and precise decision support for optimizing mining systems [41]. With the advancement of intelligent mine construction, future research can integrate cutting-edge technologies such as the Internet of Things (IoT), big data, and artificial intelligence (AI) [42; 43; 44].