The invention provides an intelligent fault diagnosis method based on a stack 
pruning sparse denoising automatic 
encoder and a 
convolutional neural network, which is called sPSDAE-CNN for short. According to the method, original input data is processed by using the stack denoising automatic 
encoder, and more training data is obtained by using a data enhancement method. The stack sparse 
pruning and 
noise reduction self-
encoder comprises a full-connection automatic encoding network, and the characteristics extracted at the front layer of the network are used for performing the operation of the subsequent layer, which means that some new connections appear between the front and rear 
layers of networks, so that the 
information loss is reduced, and more effective characteristics are obtained; meanwhile, 
pruning operation is introduced, so that the training efficiency and precision of the network are improved, higher training speed and high adaptability to 
noise signals are achieved, and the 
overfitting problem of the 
convolutional neural network is suppressed to a certain extent; according to the method, the 
flight data of the quad-rotor unmanned aerial vehicle are input into the model, and high fault diagnosis accuracy is obtained under the condition of high 
noise interference.