###### Abstract
Compared with traditional optimization algorithms, the WOA, with its strong global search capability and lower parameter tuning complexity, has demonstrated superior results in multi-modal function tests and fixed-dimensional multi-modal function tests [25]. Therefore, it was chosen as the hyperparameter optimization algorithm for the current task. In the complex problem of three machines selection, the high number and dimensionality of input variables present a significant challenge to the prediction performance of machine learning models. Effective feature selection not only improves the model's prediction accuracy but also reduces model complexity. [PERSON] et al. [26] proposed a feature selection method based on correlation coefficient mutual information (CCMI), which successfully reduced redundant information and demonstrated excellent performance across multiple datasets and classifiers. [PERSON] et al. [27] used Principal Component Analysis (PCA) to retain aggregated true signals from neighboring features while filtering out Gaussian noise, thus avoiding overfitting when executing Graph Neural Networks (GNN) on test samples. [PERSON] et al. [28] employed RF to reduce data dimensionality and obtain the best features. By predicting results using autoencoders, they shortened detection time and significantly improved prediction accuracy. These studies demonstrate the importance of appropriate feature selection in improving model accuracy and generalization ability. RF, as a model-based feature selection method, ranks the importance of input features, preserving the state and structure of the original features, preventing information loss, and enhancing the model's interpretability while maintaining accuracy. Therefore, it was selected as the feature selection method for the given data conditions. Based on the existing published research, the objectives of this paper are clearly defined as follows: (1) To comprehensively consider geological conditions and mining face conditions, explore the relationships between high-dimensional input features under actual working conditions, and identify the main factors influencing the selection of parameters for the three machines on the fully mechanized mining face; (2) To apply XGBoost to the prediction of three machines parameters, and by training on a dataset from actual production, input geological parameters and mining face conditions to automatically output the three machines parameters combined with reserve selection, reducing the bias in safety reserve selection caused by human experience. The main contributions of this study are as follows: (1) The Pearson heatmap was used to visualize the correlations between geological parameters and mining face conditions, effectively reducing collinearity among features. RF was then applied for feature selection, ultimately extracting 14 features from the initial 26 as inputs for the prediction model. This approach reduced input complexity while maintaining prediction accuracy, enhancing the model's interpretability. (2) XGBoost was utilized to effectively capture the relationships among geological parameters, mining face conditions, and equipment parameters. Furthermore, WOA was employed to optimize XGBoost hyperparameters, improving the prediction model's accuracy. Comparative analysis with existing prediction models (such as BP, LightGBM, and SVM) across multiple evaluation metrics demonstrated the superiority of the proposed model. (3) The RF-WOA-XGBoost model constructed in this study provides a practical and efficient parameter prediction framework for selecting the three machines in fully mechanized mining faces. The framework aligns with real-world operational requirements, offering theoretical and methodological support for intelligent equipment selection in the mining field.