The Software Defined Networking (SDN) technology, which has been booming in recent years, solves the prominent problems of IP networks such as layout difficulty and complex updates. In response to SDN-based traffic prediction, the chaos theory is used to reconstruct the phase space of the time series sample group. Then, the Least Squares Support Vector Machine (LSSVM) is introduced to build the SDN-based traffic prediction model, and the key parameters are optimized by the improved Particle Swarm Optimization (PSO) algorithm. The experimental results show that the model effectively improves the accuracy and error control level of SDN-based traffic prediction and is valuable in practical application.