In order to effectively and accurately track the uncertainty of the aero-engine, to improve the modelling accuracy by using the model update method, and to construct a high-fidelity numerical model of the aero-engine, this paper proposes a standard modelling method for self-updating the numerical model. Firstly, the basic features of the engine sensor outputs are acquired by learning the data set from zero to the current moment of data, and the model outputs at the next moment are predicted. Secondly, the model fine-tuning updating is used in the updating method, and the number of relevant network layers is frozen, on the basis of which the static model is fine-tuned at the top level. Finally, a series of experiments on the CMAPSS dataset demonstrate the significant effect of the method in improving the modelling accuracy.