## 1 Introduction
In this study, the XGBoost method is employed to predict and analyze machining quality, with the goal of improving prediction model accuracy and reliability, thereby supporting quality control and optimization in actual production. XGBoost captures complex data patterns through the gradient boosting framework and includes built-in functions such as missing value processing, parallel processing, and regularization, which effectively prevent overfitting and enhance generalization capability. Additionally, XGBoost's automatic feature selection and hyperparameter optimization further contribute to improving the model's performance. For these reasons, this study selects XGBoost to predict the quality of crucial processing features based on information entropy analysis, aiming to achieve efficient and accurate predictions, early warning, and enhanced product processing quality and reliability. [PERSON] et al. [27], for instance, proposed an XGBoost load forecasting model based on similar days by analyzing the influence of meteorology and day type, using regularization terms to control complexity and prevent overfitting. Their simulation results demonstrated the model's effectiveness in short-term load forecasting. However, the performance of the XGBoost model is significantly influenced by hyperparameter settings. Without proper hyperparameter optimization, the model may experience poor performance, including reduced prediction accuracy, severe overfitting or underfitting, and extended training times [28]. Hyperparameter optimization is essential to balance model complexity and generalization capability, thereby enhancing prediction effectiveness and computational efficiency. Therefore, optimizing hyperparameters is critical to fully leverage XGBoost's strengths in quality prediction [29]. Standard methods of hyperparameter optimization include grid search and random search. Grid search exhaustively explores a predefined range of parameters to find the optimal combination, which is suitable for small datasets but computationally expensive [30]. In contrast, random search explores a broader range of combinations by randomly selecting hyperparameter values within predefined iterations, though it may struggle to find the global optimal solution. Bayesian optimization, based on Bayes' theorem, can identify the global optimal solution for complex objective functions with fewer sampling iterations. For example, [PERSON] et al. [31] proposed an XGBoost algorithm based on Bayesian optimization (BH-XGBoost) for short-term wind power prediction in wind farms. Compared with XGBoost, SVM, KELM, and LSTM, BH-XGBoost demonstrated higher prediction accuracy under various conditions, especially in extreme weather and low wind speed situations, highlighting its significant advantages. Despite the proven effectiveness of combining Bayesian optimization with XGBoost in other fields, there is limited research on its application in predicting crucial quality features in machining. Therefore, this study aims to fill this gap by applying this combined approach to enhance the accuracy and reliability of quality predictions in machining processes. This study focuses on identifying key quality features in the machining of mechanical products and predicting their processing quality. As the manufacturing industry advances, the demand for higher product quality has significantly increased, particularly in the machining of parts with complex structures and multiple processes. Accurate identification and prediction of these key quality features are crucial for ensuring the performance and reliability of the final product. Traditional quality control methods often struggle to manage the complexities of modern machining environments, underscoring the need for more advanced prediction techniques. In the second chapter, the concept of information entropy is introduced to quantitatively screen key quality features, effectively addressing the uncertainty introduced by subjective factors in traditional methods.