为进一步提高地下断层识别准确率和解释效率，使用极限梯度提升树(XGBoost)机器学习算法对煤层断层进行智能识别，并结合粒子群算法(PSO)优化模型相关参数，构建基于PSO-XGBoost的断层构造识别模型。建立正演模型对PSO-XGBoost模型进行检验，并基于滇东矿区采集的实际数据对比分析PSO-XGBoost模型与PSO-RF、PSO-SVM模型的分类预测性能，选择准确率和对数损失值作为评价分类器预测模型的主要指标评价各模型的准确度。结果表明，基于PSO-XGBoost的模型在断层构造识别中展现出较高的准确率和更好的稳定性。

In order to further improve the accuracy and efficiency of underground fault identification, an intelligent fault recognition model based on the extreme gradient boosting tree (XGBoost) machine learning algorithm was constructed for coal seam faults, combined with the particle swarm optimization (PSO) algorithm to optimize the model's related parameters. A forward model was established to verify the PSO-XGBoost model, and the classification prediction performance of the PSO-XGBoost model was compared with that of the PSO-RF and PSO-SVM models based on actual data collected from the Diandong mining area. The accuracy rate and log loss value were selected as the main evaluation indicators to evaluate the accuracy of the classification prediction models for each model. The results show that the PSO-XGBoost model has a high accuracy in fault structure identification; the PSO-XGBoost model has higher accuracy and better stability in fault identification.