In order to improve the accuracy of the predictive control model, the RBF neural network is used to complete the network traffic prediction, and the model parameters are optimized by using the hybrid leapfrog algorithm in the swarm intelligence algorithm. First, the hybrid leapfrog algorithm is introduced in the modeling process. Then, the weights and thresholds of the RBF neural network are used as individual frogs, and multiple randomly combined individuals with weights and thresholds constitute a frog group. Group the frog groups, and obtain global optimal individuals through continuous regrouping and iteration within the group to obtain the optimal weights and thresholds in order to determine the optimal predictive control model. Experiments prove that the predictive control model based on RBF neural network optimized by swarm intelligence has higher accuracy.