Abstract: According to the chain flying problems easily occurred at the bending section of the scraper conveyor, based on a convolution neural network and support vector machine, a s ound and signal identification model was provided. With the PCA whitening processing of the equipment sound from fully mechanized coal mining face, the sonogram of the operation sound would be an input. The depth CNN network would be applied to pick up the sound and signal features, the SVM classifier would be applied to realize the identification of the sound and signal and finally the diagnosis on the flying chain fault of the scraper conveyor could be realized. Meanwhile, based on the SVM as the depth of an output layer, the output layer of the CNN network model at an error reverse transmission during the training could be applied to derive a sensitivity function of the full connected layer. The test showed that when the input sound and signal had a different time cutting as the input of the model and would be affected to the identification of the CNN- SVM model. Finally, the comparison test verified that the model could have a higher identification than the conventional GMM-HMM model.