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To improve the reliability of gas path fault diagnosis method, the influence of uncertainty factors such as measurement noise, individual difference and performance degradation of focus sensor is studied carefully, which leads to the problem that the false alarm rate of gas path fault diagnosis method is too high to be applied in engineering. The research of gas path fault diagnosis method based on model-based residual analysis and data-driven is carried out in this paper. Therefore, a gas path fault diagnosis framework based on the fusion of engine model analysis deviation and convolution neural network modeling theory is constructed. In the modeling process, the influence of sensor measurement bias individual difference and performance degradation on the gas path fault diagnosis results is fully considered, and the gas path fault diagnosis method combining model-based residual analysis and data-driven is formed. Then, combined with a variety of flight trajectories and air intake conditions, the numerical simulation analysis was carried out to verify the false alarm rate of the gas path fault diagnosis method. The results show that the proposed method can provide satisfactory fault diagnosis accuracy in the presence of a variety of uncertain factors and has the potential of engineering application.