爆破振动的质点峰值速度（Peak Particle Velocity，PPV）是评估爆破开采设计参数合理性的重要指标。为实现一种PPV有效预测模型，借助粒子群优化算法（Particle Swarm Optimization，PSO），结合优化极端梯度提升树（Extreme gradient boosting，XGBoost），构建了一种参数自优化的PSO-XGBoost预测模型。以LK露天铜钴矿为研究对象，选取最大单段炸药量、总炸药量、测量的爆心距、炮孔平均进深尺度和高程差共5个影响因素指标作为研究参数，通过现场收集187次爆破作业实测数据，进一步开展PPV的PSO-XGBoost预测研究，并与传统XGBoost模型、SSA-XGBoost优化模型及萨道夫斯基经验公式的PPV回归预测进行对比分析，最后利用Shapley Additive Explanatory（SHAP）方法开展影响PPV预测结果的敏感性因素分析。结果表明：PSO-XGBoost预测模型的预测评价指标计算结果最优（R²=0.921，RMSE=0.0752，MAE=0.0717，MBE=0.0683），其对PPV的预测结果明显优于传统XGBoost模型、SSA-XGBoost混合优化模型及萨道夫斯基经验公式，同时，敏感性分析得到总炸药用量是影响PPV预测结果的重要参数。进一步说明PSO-XGBoost预测模型可处理多因素的非线性特征，利用PSO-XGBoost预测模型能够更好地结合非线性、离散数据，建立一种可靠、简单有效的PPV预测模型。研究结果可为露天矿山爆破振动快速预测提供参考。

The peak particle velocity（PPV） resulting from blasting vibration serves as a crucial metric in assessing the efficacy of blasting and mining design parameters.To enhance the accuracy of PPV predictions，a novel parameter self-optimizing PSO-XGBoost prediction model is introduced，leveraging the Particle Swarm Optimization （PSO） algorithm in conjunction with optimized Extreme Gradient Boosting （XGBoost）.The research focuses on the LK open-pit copper-cobalt mine and examines five influencing factors，namely the maximum single explosive charge，total explosive charge，measured distance to blast center，average depth of blastholes，and elevation difference，as study parameters.A total of 187 sets of measured data from on-site blasting operations are gathered for further investigation into predicting PPV using the PSO-XGBoost model.The findings indicate that the PSO-XGBoost prediction model produces superior prediction evaluation metrics （R²=0.921，RMSE=0.0752，MAE=0.0717，MBE=0.0683）， compared to alternative models，including the traditional XGBoost model，the SSA-XGBoost hybrid optimization model，and the Sariakaliski empirical formula.The sensitivity analysis reveals that the total explosive charge，significantly influences PPV prediction outcomes，underscoring the importance of using an appropriate amount of explosives to avoid energy inefficiency in blasting activities. Furthermore，the research demonstrates that the PSO-XGBoost prediction model，is capable of effectively addressing the nonlinear attributes of various factors，as well as integrating nonlinear and discrete data to develop a dependable，straightforward，and efficient PPV prediction model.This study offers valuable insights for promptly predicting blasting vibration in open-pit mining and assessing the impacts of blasting activities.