This study proposes a novel semiconductor laser parameter inverse design method based on artificial neural network (ANN) and particle swarm optimization (PSO) algorithm. The ANN is trained using a laser output power as sampling data, which can be calculated by applying the traditional numerical simulation method. The network can be used to predict the power spectrum of the laser for any new values of the selected parameters. The mean square error can be as low as 0.5 mW and the CPU time as low as 0.07 s, which is about 1800 times more efficient than that of the numerical algorithm, which takes 125.57 s CPU time in the same environment. To obtain the design parameters for any target power spectrum, the inverse design can be achieved by combining this network with the PSO algorithm. It is clear from the calculation that the inverse design parameters are not unique, which proves that the semiconductor laser has a nonlinear multi-parameter problem. The combination of ANN and PSO inverse algorithm (with a mean square error of less than 0.4 mW and a CPU time of 39.45 s) demonstrates greater performance based on the same condition when compared with the traditional numerical simulation inverse method (with a mean square error of less than 0.89 mW and CPU time of 192 h). The accuracy and speed of the proposed method are improved by 22.25 times and about 17500 times, respectively.