Abstract: Combination of back-propagation (BP) artificial neural network (ANN) and genetic algorithm was used to set up genetic neural network model to predict the quality of laser cladding layer according to the laser power,powder feed rate and scan rate.An orthogonal test was designed to obtain the training data of prediction model,and then the influence of different process parameters on the cladding layer geometry quality was analyzed by the method of range analysis.The validation results show that the relative error between predicted values and experimental data is less than 4.6%,which indicated that the use of the model can accurately select cladding parameters to improve the geometry quality of the laser cladding layer of nickel-based superalloy.