Abstract: In order to improve the accuracy of the landslide displacement prediction under the complex conditions, a landslide displacement prediction model based on Radial Basis Function (RBF) neural network compensated by time-series AR was constructed. First of all, the learning and nonlinear approximate abilities of the RBF was used to extract overall trend term of the landslide displacement, then the prediction residual was obtained. After that, we constructed a prediction residual compensator based on time-series AR model. Finally, the AR predicted residual value was superimposed with the RBF approximation value to realize the landslide displacement prediction. The effectiveness of the model was verified by using the data of the landslide at water intake slope in Geheyan Hydropower Station. The results showed that after compensated by AR, the average relative prediction error was reduced from 12.718% to 4.703% comparing with the single RBF network, and the mean square error was reduced from 0.232 to 0.032. The RBF model compensated by AR is more suitable for the data series approximation at turning points and abrupt points of the landslide displacement, and has high extrapolation prediction ability.