In order to study the nonlinear characteristics of tunnel surrounding rock deformation, NAR neural network and R/S analysis method are used to analyze the deformation amount and deformation trend of tunnel surrounding rock. Through the error analysis of deformation monitoring samples by NAR neural network, it is considered that NAR neural network has small error and high accuracy in short-term prediction of surrounding rock deformation. The R/S analysis method is used to conduct rescaled range analysis of each deformation time series, and the Hurst index of each time series is obtained, the relationship between it and the deformation trend of the surrounding rock is analyzed, and the deformation trend of the tunnel surrounding rock in the example is judged by the Hurst index. The results show that the deformation of the surrounding rock in the section in the example will all show an increasing trend, but the growth range is decreasing, and the trend of horizontal convergence is stronger than that of the vault settlement, indicating that the former is less affected by random disturbance, and the stability is relatively higher in the later stage. By using R/S analysis method to analyze time series, it not only provides Hurst index criterion for predicting deformation trend of surrounding rock, but also provides a basis for stability analysis and treatment of surrounding rock.