Current research on blasting-induced peak particle velocity prediction in open-pit mines is infeasible for the hole-by-hole blasting operation using digital electronic detonators, and its models lack interpretability. By recording the blasting parameters for each blasthole and measuring the induced triaxial particle velocity, a model to predict the triaxial peak particle velocity is established based on LightGBM, and SHAP is introduced to interpret the variable importance of the model. Regarding the root mean squared error RMSE and goodness of fitting R ² on the test set, the established LightGBM model outperforms the support vector machine and neural network models as its RMSE decreases by 25.9% and 28.9%, while the R ² improves by 12.7% and 9.9%. Compared to the Sadaovsk empirical formula, which is widely applied to the blasting design and safety evaluation, the RMSE of LightGBM declines by 63.4%, 39.5% and 68.3%, while the R ² increases by 18.9%,27.7%and 42.4% in the longitudinal axis X, transverse axis Y and vertical axis Z, respectively. The SHAP values computed from the model inform that apart from the axis variable, the distance between blasting source and monitoring point, total charge, the minimum row distance, the average charge length, hole diameter and the maximum hole distance are the six most influencing variables that affect the predicted value of peak particle velocity. The distance between blasting source and monitoring point is negatively correlated with the predicted peak particle velocity, while the other five variables are positively correlated with the predicted value.