Improving the quality and diversity of generated samples has always been one of the main challenging tasks in the field of generative adversarial network (GAN). For this reason, a bidirectional constraint GAN (BCGAN) is proposed. Compared with the traditional GAN variants, this network adds one more generator module to the architecture design. The two generators approach the data distribution of real samples from two different directions. Then, according to the network architecture of BCGAN, this study designs a new loss function and analyzes and proves it theoretically. During BCGAN training, the diversity of the generated samples is enriched by increasing the distance between the data distribution of two generated samples, and the difference of the discriminator between the data distribution of the two generated samples is reduced to stabilize the training process and thereby improve the quality of the generated samples. Finally, experiments are carried out on a synthetic dataset and three open challenge datasets. This series of experiments show that compared with other generative methods, the proposed method fits real data distribution better and effectively improves the quality and diversity of generated samples. In addition, the training process of this method is smoother and more stable.