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Civil aircraft engine has precise and complicated structure. The data collected by monitoring systems contain abundant fault message. Traditional methods of monitoring engine’s health condition are based on data statistics and machine learning model. However, its performance on deep-understanding and classifying characteristics of massive data didn’t meet the requirement as we had expected. In addition, as the dimension reduction method of Neural Networks, deep learning, flourishing in recent years, builds up algorithm model which is able to process data effectively by simulating the structure of human brain. Combining the characteristics of engine with deep learning theory, the paper put forward a new method of monitoring engine’s health condition. The advantageous conditions of the method include overcoming the uncertainty of characteristic extraction and deficiency of partial response. It’s able to learn and classify the characteristics automatically. Result of the test shows that the method can not only extract characteristics from massive data, but also obtain high identification accuracy of different health conditions of engine.