Space target recognition plays an important role in the field of space security and exploration. With the rapid development of artificial intelligence technique and explosive increase of image dataset, object recognition based on deep learning has achieved favorable performance. However, the recognition of deep space targets in visible spectrum images still remains in the traditional manual interpretation approach, thus leading to low efficiency and inevitable subjective errors. In this paper, we propose an artificial intelligence method for space target recognition, called Two-Stage Convolutional Neural Network (T-SCNN). Our T-SCNN is composed of two stages, i.e., target locating and target recognition. In the stage of target locating, we first detect all suspected targets from the total image dataset by presenting a minimum bounding rectangle with threshold (MBRT) approach, then cut out all regions encompassing targets to generate target images for training. In the stage of target recognition, we send target images to the well-trained recognition network for identification. Additionally, data augmentation is conducted in the CNN training to satisfy its data quantity requirement. Extensive experiments are performed on our synthetic space target image dataset, and the result demonstrate that the proposed method achieves high accuracy within a short time.