煤矿安全应急物资储备中心选址优化是推动煤矿安全应急体系建设的重要基础，为提高煤矿安全应急物资储备中心选址的准确度和合理性，提出利用人口因素、交通因素、经济因素和自然因素，建立融合多源空间数据的煤矿安全应急物资储备中心选址机器学习组合模型，提高煤矿安全应急物资储备中心选址的准确度和科学性。利用ArcGIS分别通过渔网划分、空间链接和投影等任务对多源空间数据进行处理，并利用SMOTEENN算法避免数据不均衡的负面影响，从而构建适用于机器学习模型分析的数据集。通过对不同机器学习算法、不同特征选择方法以及不同参数寻优方法进行比较分析，得出XGBoost机器学习算法、Boruta算法和遗传算法在对煤矿安全应急物资储备中心选址分析中，相较于其他机器学习算法、特征选择方法和参数寻优方法其表现更为优异。故基于各自优势，得到煤矿安全应急物资储备中心选址的机器学习组合模型。最后引入SHAP分析方法，对不同特征影响程度、影响方向等进行分析，定量评估输入数据在决策中的贡献，增强模型可解释性。研究结果表明煤矿安全应急物资储备中心选址组合模型性能优异，准确率、精确率、召回率、F₁和A_UC分别为0.976、0.966、0.989、0.977、0.996，可为选址决策提供有力支持，模型可解释分析也能够为煤矿安全应急物资储备中心选址提供科学参考。

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.