The spatial correlation function (SCF) is an important part of the Kriging surrogate model that describes the sample data structure, and the SCF affects the fitting accuracy of the Kriging surrogate model directly. In a Kriging surrogate model, a single SCF is typically selected to describe the sample data structure, which may cause sample information loss and fitting error. A new Kriging surrogate model for combination forecasting (CF-Kriging) was constructed by integrating of linear weighted approach based on the combination forecasting method, in which the differences in the sample information described by the diverse SCFs for the same sample data structure were considered. The integrity of the sample information of the CF-Kriging model was improved using single-SCF Kriging surrogate models as sub-models and considering the minimum mean absolute percentage error as the improved target for the fitting accuracy. The effectiveness of the CF-Kriging surrogate model was demonstrated using four test functions and two engineering problems, which indicated that the CF-Kriging surrogate model could effectively improve the fitting accuracy and the fitting stability of an ordinary Kriging surrogate model.