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In view of the complex shape of glass defect, it is difficult to accurately identify the characteristics of its type. This paper proposes an integrated deep learning model to identify glass defects, which is essentially a combination of sparse coding classifier and deep convolutional neural network. Based on the auto-encoder, the model introduces the KL distance and the L1 norm as sparse terms to form a new sparse auto-encoder. The model learns the features of input sample by sparse auto-encoder and uses the trained weights as the convolution kernel of the convolutional neural network to improve the recognition speed. In the sparse coding stage, L1-L2 norm is used to replace the L0 norm, and the discriminant classification ability is added to the KSVD to make it better to classify operations, thereby improving the recognition accuracy. The experimental results show that the recognition accuracy of this method is up to 95%, which satisfy the need in on-field application and it is robust.