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In order to improve the prediction accuracy of aircraft engine performance parameters, a new aeroengine performance prediction method based on fuzzy theory and XGBoost algorithm was proposed. Through the overall performance analysis of aeroengine, the angle of throttle, altitude, total temperature, gross weight, mach number and flight phase were identified as the main factors affecting aeroengine performance; Secondly, the fuzzy theory was used to divide the QAR data into vertical flight phase data, eliminating the subjective influence on prediction accuracy, which caused by artificially dividing the training data. Finally, XGBoost prediction model of aeroengine parameters was established, and compared with various prediction models. For the prediction of aeroengine N1 and fuel flow parameters, the experimental results show that the XGBoost prediction model which does not require scaling of training data has higher accuracy than support vector regression (SVM), liner regression models and BP neural network.