In order to solve the problem of large number of factors, multicollinearity between factors and spatial correlation among measuring points in the spatio-temporal monitoring model of super-high arch dams, the kernel independent component analysis (KICA)method was used to extract independent components based on the prototype monitoring data of dam deformation, and the information of multiple measuring points was transformed into a few comprehensive indicators. On this basis, the support vector machine (SVM) model was optimized by using the advantages of grey wolf optimization (GWO) algorithm with good convergence speed and solution accuracy in parameter optimization. The extracted independent components were substituted into the SVM model optimized by GWO algorithm, the regression prediction on the spatial measuring points of the super-high arch dam was performed, and the KICA-GWO-SVM spatio-temporal monitoring model for super-high arch dams was constructed. Analysis of engineering examples shows that, compared with the multiple regression model, BP model and SVM model, the KICA-GWO-SVM spatio-temporal monitoring model has strong nonlinear expression ability and good performance, which can reduce the influence of multicollinearity on dam deformation monitoring, and the fitting and prediction accuracy of the deformation sequence of super-high arch dams is excellent. It can more accurately and comprehensively grasp the overall spatio-temporal deformation behavior of the dam.