In order to solve the problem the sub-network output can not be optimally integrated in a modular neural network(MNN), this paper proposeds a dynamic MNN based on the particle swarm optimization(PSO) algorithm. Firstly, the distribution of samples can be identified and the center of datas can be updated by computing the data density. Secondly, the corresponding sub-networks are activated according to the input datas, then the output weights are calculated by the best contribution degrees which are computed via the PSO algorithm. Finally, a dynamic neural network is completed to optimize the integrated output of the MNN. Based on the approximating experiments of the non-linear function and time-series prediction, it is proved that the number of sub-networks can be adjusted dynamically, and the integrated weights of the neural network can be optimized by using the PSO algorithm. Comparisons with other algorithms demonstrate that the proposed method is more effective in terms of the accuracy and adaptive ability.