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To solve the problem that subnetwork output cannot be optimally integrated in a modular neural network (MNN), this paper proposes an adaptive particle swarm optimization algorithm for dynamic MNN (APSO-DMNN). First, the method identifies the distribution of samples and updates the training parameters based on data potential. Second, the MNN activates the corresponding subnetworks according to the input data. Calculate the weights based on an APSO algorithm, which can dynamically optimize the contribution of the output. Then, the inertia weights in the APSO algorithm are adjusted by a nonlinear function in order to avoid being trapped into local optimal values. Finally, the proposed APSO-DMNN can be obtained based on the optimal integration and dynamic adjustment. Comparisons with other algorithms indicate that the proposed method is more effective in modeling and predicting.