In specific application, the Gaussian Process Regression (GPR) algorithm depends too much on the selection of the kernel function and has limited space for improving the accuracy.In order to obtain higher prediction accuracy, this paper proposes a Gaussian Process Regression (GPR) prediction algorithm combined with AdaBoost.RT algorithm.The algorithm introduces the concept of preset threshold from the perspective of statistics, and divides the GPR prediction results of different kernel functions into two parts, i.e.correct and error.After two layers of training, the credibility of the GPR algorithm with different kernel functions is reflected by the final weight.Fninally, combination is made to each kernel function, and the output with high-quality prediction value is obtained.In the failure prediction simulation experiment of lithium battery, the prediction error of the algorithm is reduced by 82.14% compared with the traditional GPR algorithm, which proves the rationality and practicability of the method.