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Aeroengine is the core power system of the aircraft. The working environment is harsh. The state monitoring and life prediction are important technical means to ensure the safe and reliable operation of the aircraft. This paper proposes a method for predicting the remaining life of aeroengine based on stacked sparse autoencoder. Firstly, multiple self-encoding networks are connected to form a deep stack self-encoding network, and the state data of the engine is selected as the training input of the network to make the network layer-by-layer intelligent extraction. Distributed rules between data to build an engine-degraded stacked self-encoding learning model. The BP residual neural network is used to classify the remaining life of the engine as a result of the prediction of the remaining life of the engine. The proposed method is validated by using the engine degradation data in the PHM2008 Challenge. The results verify the effectiveness of the stacked self-encoding network deep learning method for the prediction of remaining life of aeroengine.