Abstract: The site selection optimization of coal mine safety emergency reserve center is an important foundation for promoting the construction of coal mine safety emergency response system. In order to improve the accuracy and reasonableness of coal mine safety emergency reserve center site selection, it is proposed to establish a machine learning combination model for coal mine safety emergency reserve center site selection, integrating multi-source spatial data by using demographic, transportation, economic and natural factors to improve the accuracy and scientificity of coal mine safety emergency reserve center site selection. The accuracy and scientificity of coal mine safety emergency reserve center site selection are improved. Firstly, the ArcGIS is used to process multi-source spatial data through tasks such as fishing net division, spatial linking and projection respectively, and the SMOTEENN algorithm is utilized to avoid the negative impact of data imbalance, so as to construct the dataset applicable to the analysis of machine learning model. Secondly, by comparing and analyzing different machine learning algorithms, different feature selection methods and different parameter optimization methods, it is concluded that the XGBoost machine learning algorithm, the Boruta algorithm and genetic algorithm have better performance than other machine learning algorithms, feature selection methods and parameter optimization methods in the site selection analysis of coal mine safety and emergency reserve center. Therefore, based on the advantages of each algorithm, this paper obtains a combined machine learning model for coal mine safety emergency reserve center site selection. Finally, the SHAP analysis is introduced to analyze the influence degree and direction of different features to quantitatively assess the contribution of input data in decision-making and enhance the interpretability of the model. The results show that the combined model of coal mine safety emergency reserve center siting has an excellent performance, with 0.976, 0.966, 0.989, 0.977, 0.996 in accuracy, precision, recall, F₁ value and A_UC, respectively, which can provide a powerful support for siting decision-making, and the model interpretable analysis can also provide a scientific reference for coal mine safety emergency reserve center siting.