The surface damage to a spacecraft in orbit may have serious consequences， and thus real-time damage inspection is required. In order to solve the problem that spacecraft damage image samples are difficult to obtain， in the paper， a generative adversarial network （GAN） for spacecraft surface damage based on small sample data augmentation is proposed by means of the intelligent inspection method. The network can learn the feature texture representation of a single input image， and generate a large number of fine-grained samples similar to the features of the input image， thus realizing the expansion of a small number of image data samples. The YOLO object inspection algorithm is used to inspect and identify the surface defects and damage in the expanded image samples， and high inspection precision is obtained. The proposed network can provide technical support for the future spacecraft health monitoring and evaluation， the application of generalized service robots， and the in-situ construction of space.