In order to better solve large-scale flexible shop scheduling problems and improve the searching performance of flexible shop scheduling algorithms, a hybrid particle swarm optimization(HPSO) algorithm based on entropy was proposed, which combines the particle swarm optimization, genetic algorithm with simulated annealing algorithm, and the inertia factor and mutation probability were adjusted adaptively according to population entropy in order to enhance the searching ability of the algorithm and overcome the premature convergence of the algorithm. Simulation results on benchmark instances have shown that the proposed algorithm can solve flexible shop scheduling problems, and has obvious advantages in the accuracy of optimization over traditional optimization algorithms.