The accurate maintenance level decision can avoid the excessive maintenance and shortage of maintenance and save maintenance cost on the premise of ensuring the safe operation of aero-engine. To achieve the classification and prediction for maintenance level

monitor information and characteristics of maintenance level

using algorithm of deep belief network（DBN）

were combined

excavating deep relationship between the condition monitoring and maintenance level decision making. The model can extract sample feature from DBN pretreatment and back propagation (BP) neural network reverse fine-tuning and improve the forecast accuracy of maintenance level. Taking the state parameters and maintenance level data of an airline CF6 engine as example

the analysis results showed that the model could excavate the deeper information of the sample through the construction of multi-layer network structure

which was superior to the traditional neural network in the classification ability and the accuracy of decision-making. So the model had strong ability of feature extraction and higher classification accuracy for the maintenance level. The model was able to get more accurate results maintenance level decision and avoided unnecessary losses due to misclassification of maintenance level.