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Deep learning is a new machine learning method based on feature representation. The deep learning model consists of multiple hidden layers, and the feature information in the hidden functional layer can be obtained by automatically learning the input data. Compared with traditional diagnostic methods, deep learning has the ability to extract more abundant features from the original information, so it has become a new area of machine learning-based fault diagnosis research. It brings new idea of the complex system fault diagnostic such as aero-engine gas path. Combining the characteristics of complex system test data and the advantages of deep learning, a fault diagnostic method based on convolutional neural network is proposed, including preprocessing, model training and optimization. Then a complex system fault diagnostic algorithm platform based-on deep learning method is realized. The simulation method of an aero-engine gas path test proves that the proposed method has good feasibility and effect, it can make full use of the advantages of deep learning and more accurately identify the health state of the aero-engine gas path.