China is a big country in the production and consumption of poultry eggs, and testing the quality of poultry egg is very important. Machine vision plays an important role in non-destructive testing of poultry eggs. Computer vision is one of the commonly used technical methods in the field of non-destructive testing of poultry eggs. This technology needs to collect a large number of poultry egg image data for analysis and modeling in order to obtain a good recognition effect. When doing research on non-destructive testing of poultry eggs, researchers need to spend a lot of manpower and material resources on the collection of image data of poultry eggs. Aiming to solve this problem, an improved egg image data generation network based on unsupervised representation learning with deep convolutional generative adversarial networks (DCGAN) was proposed. The network was divided into a generator and a discriminator. The generator was used to generate the egg image data. The discriminator judged the authenticity of the generated egg image. The generator and discriminator competed with each other and finally generated high-quality egg image data. In order to improve the quality of the generated egg images, a residual network was used to construct a generator and discriminator, the loss functions of Wasserstein distance and gradient penalty were introduced to research the image generation in the case of egg transmission and egg reflection. This method effectively solved the problem that it was difficult to collect a large number of poultry egg image data, provided a data basis for the later identification and detection of poultry egg image, and also provided technical support for the subsequent establishment of poultry egg database.