In the process of traditional wavelet network for parameter optimization, the gradient descent method is easily to produce the local optimum. To solve this problem, an improved quantum behavior of QPSO algorithm was proposed. In the proposed method, a weighted coefficient was added to improve the global and local search and convergence speed of PSO. When the evolution became premature, particle swarm began to mutate in this dimension. The reinitialized position of the particles in the dimension reuniformly was distributed in the feasible region for improving search accuracy. The simulation results show that the improved QPSO algorithm outperformed in the searching ability than conventional network training method.