Abstract: Compared with loose blasting, throwing blasting has higher vibration intensity and lower frequency, which has a greater impact on the safety production of open-pit mines. To accurately predict the vibration velocity of throwing blasting, literature review data and on-site monitoring data were used to analyze the characteristics of throwing blasting vibration velocity related data. Pearson correlation analysis was used to clarify the key influencing factors of throwing blasting vibration velocity, and a prediction index system for throwing blasting vibration velocity was constructed. We separately constructed a genetic algorithm optimized least squares support vector machine (GA-LSSVM) model and an Elman neural network optimized adaptive enhancement algorithm (Elman Adaboost) model, and integrated the two using the inverse error method to form an integrated prediction model for throwing blasting vibration velocity. We also proposed evaluation criteria and testing methods for the models. The results indicate that the distance from the blasting center, height difference, number of rows, total charge, explosive consumption per unit, and hole spacing are the main factors affecting the vibration velocity of throwing blasting. Optimize to determine a maximum iteration count of 100, activation function of Relu, random number seed of 42, number of neurons of 30, and data allocation ratio of 8∶2. Compared with the single model, the integrated prediction model can overcome the limitations of the traditional single prediction model, have better information capture ability, and improve the robustness and prediction accuracy of the vibration speed prediction model. The evaluation index determination coefficient (R²), root mean square error (R_MSE), and mean absolute error (M_AE) of this model are 0.957, 4.382, and 2.173, respectively. Compared with GA-LSSVM and Elman Adaboost models, R² has increased by 5.51% and 12.34%, R_MSE has increased by 15.88% and 25.63%, and M_AE has increased by 35.99% and 33.34%, respectively.