## 1 Introduction
[20] constructed an optimization framework for backfill mining production scheduling using mixed-integer linear programming. Additionally, in 2019, [PERSON] et al. [21] proposed a boundary grade optimization model for underground polymetallic mines, further expanding the application scope of [PERSON]'s theory. These studies have made substantial contributions to enhancing the production efficiency of underground metal mines, reducing costs, and promoting sustainable development through the application of various mathematical programming methods. They offer valuable theoretical and practical references for tackling challenges in the production process. Although mathematical programming methods have achieved remarkable results in optimizing the production process of underground metal mines, they also possess certain limitations. Firstly, these methods typically depend on precise mathematical models and well-defined constraint conditions. However, the actual production process is highly complex, involving numerous uncertain factors, which makes it challenging to construct an accurate model using purely mathematical approaches. As a result, the model may not fully align with real-world scenarios, thereby impacting the accuracy and practicality of the optimization outcomes. Secondly, when addressing large-scale and high-dimensional problems, the computational complexity of mathematical programming methods increasessubstantially, leading to significantly extended solution times. This limitation restricts their applicability in more intricate production environments. In contrast, machine learning methods exhibit distinct advantages in production process optimization [22]. Machine learning methods can automatically uncover potential patterns and rules in data by learning from a substantial volume of actual production data, without the necessity of pre-constructing an accurate mathematical model [23,24]. They demonstrate stronger adaptability to complex and highly uncertain production environments. When handling large-scale data, machine learning algorithms can efficiently conduct data analysis and processing with the support of technologies such as distributed computing, significantly reducing calculation time and enhancing optimization efficiency [25]. Moreover, machine learning models possess excellent adaptability and dynamic learning capabilities, enabling them to update and optimize the model in real time based on newly generated data. This allows the models to promptly adapt to various changes in the production process and effectively meet the demands of continuous production process optimization [26]. To further investigate the weak links in the underground mining system, this study gathered data from 88 slopes in Tongkeng Mine, Guangxi Province, China, over a decade to construct a comprehensive dataset. By employing machine learning techniques, this study incorporated the key influencing factors of the mining system, established an accurate regression analysis relationship with the production capacity of the mining system, and utilized the SHAP interpretability model to rank and compare the influencing factors regarding the production capacity of the mining system. The objective was to identify the weak links in slope mining efficiency and provide critical insights for the adaptive adjustment of slope mining technology.