A selective laser sintering (SLS) technique uses a method called finite element simulation to forecast and analyze temperature fields. However, the temperature field simulation computation takes a long time. An SLS sintering points temperature prediction approach, based on a genetic algorithm (GA) optimized back propagation (BP) neural network, is proposed to enhance the computation efficiency. A large number of simulation experiments of sintering point temperatures of coated sand multitrack-multilayer parts were conducted. A sintering point temperature prediction model based on GA-BP neural network was created and trained based on the above experiments. A piece of software for predicting SLS sintering point temperatures was developed. The software can quickly calculate and visually display the sintering point's temperatures based on the dimension and process parameters. The accuracy of temperature prediction was confirmed when the predicted and detected sintering point temperatures of the parts were compared experimentally.