## 5 Discussion  
The RF-WOA-XGBoost-based parameter prediction model for fully mechanized mining equipment proposed in this paper demonstrates significant advantages in multiple aspects, including parameter prediction speed, input feature complexity, model interpretability, and prediction accuracy. By systematically integrating geological parameters and mining face conditions, the model not only effectively improves the efficiency of parameter prediction but also enhances the match with actual operating conditions, providing an intelligent and efficient solution for equipment selection. Compared to traditional methods that rely on expert experience, this paper significantly reduces the impact of subjective judgment and, through RF, ranks the importance of 26 input features, selecting key influencing factors (such as coal seam thickness and roof type). This approach reduces the complexity of input variables while ensuring the model's efficiency. Figure 7: Comparison of predicted values and actual data values for different features under different algorithms, they should be listed as: (**a**) Minimum support height of hydraulic support; (**b**) Maximum support height of hydraulic support; (**c**) Support strength; (**d**) Working resistance; (**e**) Fully mechanized mining face cycle yield; (**f**) Number of cuts; (**g**) Cutting depth; (**h**) Average hauling speed; (**i**) Maximum hauling speed; (**j**) Maximum production capacity; (**k**) Installed power; (**l**) Conveying capacity. Compared to existing intelligent selection methods, this approach further enhances the comprehensiveness of the model and its adaptability to complex working conditions by integrating geological parameters and mining face conditions. In addition, the importance ranking from RF ensures the model's interpretability, providing transparent reference criteria and addressing the limitations of existing \"black-box models\" in industrial practice. The introduction of the WOA further optimized the XGBoost model's hyperparameters, achieving an R\({}^{2}\) value of 0.9451 and a MAE of 0.0458 on the test set, outperforming both the non-optimized XGBoost model and other comparison algorithms (LightGBM, BP, and SVM). The results demonstrate that the RF-WOA-XGBoost method exhibits greater robustness and adaptability in complex data environments. As shown in Table 8, a comparative analysis of traditional methods and existing intelligent selection methods highlights the core characteristics and deficiencies of each approach, further illustrating the advantages of the proposed model. Despite these significant advancements, this paper still has certain limitations. The data used in this research primarily focus on lightly inclined fully mechanized mining faces, and the model's applicability in more complex working conditions, such as high gas content, deep coal seams, and steep mining faces, has yet to be verified. Moreover, the current paper is limited to parameter prediction and has not implemented the automated output of equipment that meets the parameter requirements, thus limiting the full-process intelligent application of three machines selection. Future research could expand the data sample range to include more complex dynamic conditions to validate the model's wide applicability. Additionally, combining other optimization algorithms could allow for the automated matching of equipment that meets parameter requirements, further enhancing the level of intelligence in the selection process. To achieve full-process intelligent selection, future studies could also explore the integration of the model with real-time sensor data, improving its adaptability to dynamic environments, and investigate the potential applications of this method in other fields (such as tunnel engineering or oil and gas drilling equipment selection), thereby promoting the development of intelligence in multiple engineering domains.