Gaussian Process (GP) is characterized by the non-linear property, which leads to too high training time complexity for a large sample, And the hyper-parameters directly affect the prediction accuracy of Gaussian Process. The method of improved GP optimized by the artificial bee colony (ABC) algorithm is proposed to reduce the time complexity and to improve the prediction accuracy. Improved GP constructs the model by selecting a sub-sample of training samples to reduce training time. ABC optimizes the hyper-parameters of improved GP to improve prediction accuracy. Firstly, the improved GPR model is constructed by selecting a sub-sample of training samples; then it is followed by ABC algorithm searching the optimal hyper-parameters of improved GPR; finally the test sample is used to predict and output the prediction accuracy. The model is applied to solve maritime long-range precision sea strike (LPSS) system-of-systems operational effectiveness evaluation issues, and the MATLAB simulation experiments verify the validity of the model compared with other evolutional algorithms.