An intelligent diagnosis algorithm was presented based on double structure deep learning to identify the bearing fault type and damage degree.In the method, an incomplete data model was used and divided into two structures: fault type self-learning network and fault damage degree identification network.In the first structure, the faulty signal was filtered by morphology to suppress some noises and enhance the impulse components.Then, the filtered signal was transformed by S-transform to obtain a time-frequency graph, and obtain the common features of the fault type.The time-frequency graph was used as the input of a convolution neural network (CNN) to gather the same type of samples and separate the different types of samples in the target space by virtue of the similarity of the network, and further to realize the classification of bearing fault types and the self-learning of new fault types.In the second structure, the signals of known fault types were employed as the input of a deep belief network (DBN) after normalization, in order to extract the different features of different damage degrees.Subsequently, the extracted features were utilized as the input of a Bayesian classifier to automatically recognize the fault damage degree according to the posterior probability classification rules.In the end, the proposed method was validated using the bearing fault data acquired by the Bearing Data Center supported by the Case Western Reserve.The results show the proposed method can not only accurately identify fault type and damage degree, but also realize fault type  self-learning and damage degree self-growth under the condition of incomplete data modeling, and enhance the intelligence of the diagnosis procedure.