Combining time series analysis, differential evolution (DE), grey wolf optimizer (GWO), and support vector regression (SVR), a new prediction model of tunnel deformation was proposed, in order to accurately predict tunnel deformation has the characteristics of non-equidistant and complex nonlinear. Firstly, non-equidistant data was processed equidistantly by cubic-spline function interpolation. Then based on the time series principle, the deformation were decomposed into the trend terms and stationary random terms. The proposed model was used to predict the two terms. Finally, the displacement components were superimposed to realize the prediction of the tunnel cumulative deformation. Taking the measured vault settlement of Xinglong Tunnel in Chongqing as an example, the vault settlement of the front ZK37+900 and ZK37+910 section was predicted and compared with existing models. Results showed that the root-mean-square error were 0.193 7 mm and 0.086 9 mm, the mean-absolute-percent error were 1.21 % and 0.55 %, and the correlation coefficient were 0.997 1 and 0.992 8. Compared with the existing models, the proposed model has higher prediction accuracy and smaller error, it has better applicability and application prospects.