Image classification plays a vital role in the field of computer vision. Many existing image classification methods with high accuracy are based on supervised learning, which requires a great number of labeled images. However, the labeling of images requires a lot of human and material resources. In this paper, we focus on semi-supervised image classification, which can build a classifier using a few labeled images and plenty of unlabeled images. We propose an attention-based generative adversarial network (GAN) for semi-supervised image classification, which can capture global dependencies and adaptively extract important information. Furthermore, we apply spectral normalization, which can stabilize the training of attention-based GAN. The experimental results obtained with the CIFAR-10 dataset show that the proposed method is comparable with the state-of-the-art GAN-based semi-supervised image classification methods.