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Based on Generative Adversarial Networks (GANs), most current text-to-image generation algorithms focus on designing different attention generation models to improve the characterization and expression of image details. However, they ignore the discriminator􀆳s perception of key local semantics, so the generation models can easily generate poor image details to "fool" the discriminators. This paper designs a vocabulary-image discriminative attention module in the discriminators to enhance the discriminators􀆳 ability to perceive and capture key semantics, and drive the generation model to generate high-quality image details. Therefore, a discrimination-enhanced generative adversarial model (DE-GAN) is proposed. The experimental results show that, on the CUB-Bird dataset, DE-GAN achieves 4.70 on the IS index, which is 4.2% higher than the baseline model and achieves high performance. [ABSTRACT FROM AUTHOR]