Text-to-image algorithm requires high image quality and text matching. In order to improve the clarity of generated images, a generative adversarial network model is improved based on existing algorithms. Dynamic memory network, detail correction module (DCM), and text image affine combination module (ACM) are added to improve the quality of generated images. Specifically, the dynamic memory network can refine fuzzy images and select important text information storage to improve the quality of images generated in the next stage. DCM corrects details and repairs missing parts of composite images. ACM encodes original image features and reconstructs parts irrelevant to the text description. The improved model achieves two goals. On the one hand, high-quality images are generated according to given texts, with contents that are irrelevant to the texts preserved. Second, generated images do not greatly rely on the quality of initial images. Through experiments on the CUB-200-2011 bird data set, the results show that compared with previous algorithm models, the Frechet inception (FID) has been significantly improved, and the result has changed from 16.09 to 10.40, which proves that the algorithm is feasible and advanced.