Due to the complexity and variability of atmospheric and vacuum distillation process, the coupling between process variables is serious, and the direct modeling will increase the difficulty of problem analysis. In order to improve the performance of the model, KPCA algorithm was used to select the variables of the model, and then the processed data were used as the input of the Gaussian process regression (GPR) model, and KPCA?GPR was used to establish the estimation model of the gasoline dry point on the atmospheric tower roof. The method solves the strong nonlinear correlation between different variables, and has the advantages of flexible nonparametric generalization and super?parameter adaptive adjustment. By calculating the empirical confidence interval, not only can the dry point of gasoline be predicted and estimated, but also can do the probability interpretation. The simulation results show that the KPCA?GPR method achieves better estimation results.