This paper presents a hybrid method to predict tunnel surrounding rock displacement, which is one of the most important factors for quality control and safety during tunnel construction. The hybrid method comprises two phases, one is support vector machine (SVM)-based model for predicting the tunnel surrounding rock displacement, and the other is GA-based model for optimizing the parameters in the SVM. The proposed model is evaluated with the data of tunnel surrounding rock displacement on the tunnel of Wuhan-Guangzhou railway in China. The results show that genetic algorithm (GA) has a good convergence and relative stable performance. The comparison results also show that the hybrid method can generally provide a better performance than artificial neural network (ANN) and finite element method (FEM) for tunnel surrounding rock displacement prediction.