Abstract: In order to obtain more process parameters for laser dressing bronze diamond grinding wheel according to experimental rules, this paper uses back propagation(BP)neural network, particle swarm optimization and genetic algorithm (PSO&GA) to establish a prediction model for laser dressing bronze diamond grinding wheel. Firstly, by analyzing the principle of laser dressing, the grinding wheel profile surface angle, laser deflection angle, incidence angle and spot overlap rate were obtained as the main influencing parameters, and 192 sets of process test data were trimmed with the grinding wheel surface angle error and peak-to-valley (PV)value as the evaluation index. Then, a 4×9×2 three-layer BP neural network prediction model was established, and the predictive model was trained and optimized by the PSO&GA hybrid optimization algorithm. Finally, 16 sets of experimental data were selected to test the BP neural network prediction model, and the prediction results were more accurate, and the training effects of the BP neural network by gradient descent (GD), particle swarm optimization (PSO) and genetic algorithm (GA) were compared. The results show that the angle error prediction bias of the BP neural network trained by the PSO&GA hybrid optimization algorithm is within 0.2°, and the prediction deviation of PV value is within 1.6 μm, and compared with other optimization algorithms, the BP neural network has a faster convergence speed and better convergence accuracy. It provides a good predictive model for laser dressing of bronze diamond grinding wheels.