The deformation of building structures or rock mass usually has the features of complexity and nonlinearity that the general regression model cannot accurately predict. In this paper, gaussian process regression(GPR) theory is applied in time series analysis of nonlinear deformation monitoring data. Considering the unceasing updates and massive accumulation of monitoring data, the hyper-parameter and the adaptability of sample set, a "progressive~truncation type" hyper-parameter automatic update mode and selection method for training sample set was developed. On this basis, a GPR time-driven deformation intelligent prediction model(GPR-TIPM) was constructed. This model was applied to the nonlinear time series analysis of monitoring points on a mine slope. By analyzing the deformation trend, a composite kernel optimization method including the "Matérn32" and square exponential covariance kernel function is proposed. The experimental results showed that the prediction performance of the combined kernel function is better than that of the single kernel function, and improved the generalization ability of the model The prediction effect of GPR-TIPM model is better in the short term.