For high-speed mobile multiple-input multiple-output orthogonal frequency division multiplexing system, a low-complexity time-varying channel prediction method joint the back propagation(BP) neural network with basis expansion model is proposed. To reduce the computational complexity, the basis expansion model is employed to model the time varying channel, and the channel information at a future time is obtained by the offline training and online prediction of the channel base coefficient. During offline training, the proposed method first acquires the channel base coefficient by the received pilots. Then to obtain the channel prediction network model, the training sample is constructed and sent into the BP neural network for training. During the online prediction, based on the network model and historical base coefficient estimation obtained by the training, the proposed method can obtain the time domain channel at the future time. The simulation results show that the proposed method has lower computational complexity and higher prediction accuracy than the existing methods, which is suitable for the efficient acquisition of time-varying channel information in the future high-speed mobile environment.