The effective monitoring of aero-engine gas path system status has always been one of the technical bottlenecks plaguing the industry. To improve the effectiveness of aero-engine gas path system condition monitoring, this paper proposes a new method combining to monitor the health of the aero-engine gas path system, the independent component analysis (ICA) and deep learning phase. Firstly, the actual collected health monitoring parameters of aero-engine gas path system are preprocessed. The preprocessed parameter data is processed by independent component analysis method to extract the characteristic coefficient matrix representing the current state. Secondly, the deep learning state monitoring model is designed and established by the extracted feature matrix. Finally, the established state monitoring model is used to monitor the health status of the aero-engine gas path system. In order to show the effectiveness of the proposed method, the traditional neural network and support vector machine are used to monitor the state of the principal components analysis (PCA) and the ICA feature extraction matrix. The research shows that the state monitoring method combining independent component analysis and deep learning can be used and it is very suitable to realize the condition monitoring of the aero-engine gas path system. The effectiveness of condition monitoring is obviously better than other methods, and it has a good application prospect.