###### Abstract
[PERSON] et al. [16] used geological parameters for geological similarity threshold judgment and outputted similar historical results that met the requirements. [PERSON] et al. [17] proposed an expert system suitable for three-machine selection in thin coal seams. The system inputs geological parameters, combines an expert knowledge base to output theoretical parameters, and uses numerical simulation to increase the reserve selection for hydraulic support parameters, comparing with a database to output machine models. [PERSON] et al. [18] classified different regions and selected mining face condition parameters within a fixed range, using theoretical calculations and combining surrounding mine examples to output equipment models. These methods have improved the efficiency of equipment selection to varying degrees, but they generally have certain limitations, including failing to comprehensively consider all related factors or their interrelationships, which results in overly simple or complex model inputs. The establishment of simple rules for reserve selection or reliance on surrounding mines leads to incomplete references for reserve selection. To overcome the limitations of traditional equipment selection methods, modern technologies have been widely applied in equipment selection, integrating advanced algorithms, data-driven models, and domain expertise to optimize decision-making and performance. [PERSON] et al. [19] proposed an intelligent method for selecting tunnel-boring machine cutters, which combines Particle Swarm Optimization (PSO) and Backpropagation (BP) algorithms. This method aims to improve the efficiency of cutter selection and enhance performance under different geological conditions by combining optimization techniques and machine learning models. [PERSON] et al. [20] focused on addressing the complexity and dynamics of drilling equipment selection. By integrating machine learning algorithms with real-time data, they developed an automated recommendation system that optimizes equipment configuration and improves operational efficiency. Many studies have shown that machine learning-based intelligent selection methods can effectively reduce the number of selection changes, compress development cycles, and lower costs. These advantages provide theoretical support for the prediction methods employed in this paper. In recent years, XGBoost, as an efficient machine learning algorithm, has been widely adopted in prediction tasks, and its algorithm that integrates hyperparameter optimization further enhances the performance of the model. [PERSON] et al. [21] proposed a heart disease prediction and diagnostic system based on XGBoost, aimed at the early prediction of heart disease and assisting cardiologists in improving the diagnostic process. The system utilizes Bayesian optimization to fine-tune the hyperparameters of XGBoost, which is key to improving the effectiveness of the classifier. [PERSON] et al. [22] introduced an improved Genetic Algorithm (GA), which, when combined with dimensionality reduction and hyperparameter optimization of XGBoost, is applied to the task of predicting junk information for imbalanced datasets. [PERSON] et al. [23] proposed the GS-GA-XGBoost model, which effectively optimizes the hyperparameters of the XGBoost algorithm by combining Grid Search and GA, greatly enhancing the accuracy of porosity prediction. [PERSON] et al. [24] optimized the hyperparameters and structure of the XGBoost model through Particle Swarm Optimization (PSO), significantly improving the model's accuracy in network intrusion detection. These studies show that XGBoost, combined with optimization algorithms for hyperparameters, performs excellently in many complex prediction tasks, effectively addressing challenges such as high-dimensional data, complex features, and imbalanced datasets.