Aiming at the method of adjusting weights, parameters and the structure of the network, an improved algorithm for the small-world neural network is proposed based on topology optimization. Simulation results show that the novel network model has a better performance of fast convergence rates, high approximation accuracy and strong stability on the comparison with different kinds of existed small-world neural networks.