In order to reduce the crack defects of the cladding layer of broadband laser solid forming, this study takes the minimum crack density as the optimization goal, through respectively response surface method design and GA -BP neural network model of crack defects optimized by genetic algorithm, the crack defect prediction model and the combination of process parameters with the least crack density are obtained. Compare the optimization results of the two. The results show that the crack density obtained by the response surface method is 0.075 mm/mm², the crack degree obtained by the test under this parameter is 0.077 486 mm/mm², and the relative error is 3.21%. The minimum crack density obtained by optimizing the process parameters of GA-BP neural network model is 0.057 2 mm/mm², the experimental value is 0.058 123 mm/mm², and the relative error is 1.59%. The effectiveness of parameter optimization of GA-BP neural network in the actual laser solid forming process is verified, which provides a theoretical basis for effectively eliminating or reducing crack defects.