Abstract In hybrid welding, due to the large number of welding parameters and the coupling between different welding parameters, any change of welding parameters will have a significant impact on the weld section size, so it is always essential to choose reasonable welding parameters to improve the stability of the weld. In this paper, an automatic measurement system for weld section size is designed. The center point of the weld section contour is taken as the origin of the polar coordinate system, and the pixel coordinates of the boundary points on the weld section contour are detected every 15 degrees. Based on 32 groups of laser-arc hybrid welding experiments, the BP (Back Propagation) neural network is used to establish the prediction model between the input parameters (welding current, laser power, welding Angle, welding gap, and weld blunt) and the output weld section size. Aiming to the problem of too many input and output parameters of the BP neural network, a multiple population genetic algorithm (MPGA) is introduced to optimize the internal weights and thresholds of the BP neural network to improve the prediction accuracy. Finally, the difference between the dimensions of the left and right sides of the weld is calculated as the symmetry of the weld profile. The results show that the measurement accuracy of the automatic measurement system can reach 98%, and the prediction accuracy of the section size and symmetry can reach about 90% with the optimized BP neural network. The research method in this paper is of great significance to the study of welding process parameter optimization.