To further enhance the accuracy of orbit passenger flow prediction, a fireworks algorithm(FWA) -particle swarm optimization(PSO)-back propagation(BP) orbit passenger flow prediction model was proposed. Random factors were introduced through PSO algorithm into the evolutionary equation. However, the local search function of PSO algorithm might be weakened by this random search mode, easily leading to premature convergence and insufficient optimization power. In order to improve this problem, the explosion spark and mutation spark in the fireworks algorithm were introduced to dynamically adjust the search range and number of particles, enhance the diversity of the particle swarm, and make the particle swarm algorithm have a self-regulation mechanism of local search ability and global search ability, thereby improving the premature convergence problem of the particle swarm algorithm. The initial weights and thresholds of the backpropagation(BP) neural network were better optimized. Using Chongqing rail passenger flow data as an example, the results show that the FWA-PSO-BP model has a mean absolute percentage error(MAPE) of 2.54%, which is superior to all other compared models.