Objective To investigate the optimization effect of the remanufacturing process parameters of the HT250 matrix under different optimization algorithms. Methods Experiments were designed using a factorial design based on a Taguchi L18 orthogonal array. The surface defects of HT250 substrate were repaired by sub laser instant cladding technology, and a hybrid method that included the response surface methodology (RSM)-back propagation neural network (BPNN)-integrated simulated annealing algorithm (SAA) was proposed to search for an optimal parameter setting of the remanufactured HT250 matrix, and the effects of input power, processing time, velocity and gas flow on the tensile strength of the remanufactured sample were also analyzed in detail. In addition, the optimization results, stability and veracity were analyzed to compare the results of BPNN integrated SAA with that of the RSM approach. Results The optimal remanufactured HT250 matrix conditions were input power of 2960 W, processing time of 0. 6 s, speed of 6 mm / s, gas flow of 3 L / min. The maximum tensile strength of the remanufactured sample under these conditions was 230. 52 MPa. Conclusion The results showed that the tensile strength was significantly influenced by the input power P and single repair time t, while the influences of other factors were weak. The BPNN / SAA method was more effective than RSM for the optimization of remanufactured HT250 matrix.