In view of the lack of samples caused by the difficulty in obtaining crack images and the insufficient ability of traditional data expansion methods to enhance the sample feature space, a crack sample expansion method based on modified deep convolutional generative adversarial network (MDCGAN) was proposed. Firstly, the data set was preprocessed, and the sliding window method was used for data dimension reduction and cleaning. Secondly, the activation function was optimized to improve the diversity of generation features. At the same time, spectral normalization was introduced for weight standardization to improve the stability of network structure, so as to generate high-quality crack data set. Finally, the improved Alexnet network was used to extract and classify the extended mixed sample set. The results show that the data enhancement performance of MDCGAN is significantly improved compared with the traditional expansion method, which is suitable for expanding crack images.