A text-to-image generation algorithm based on coordinate attention mechanism and generative adversarial network (CAT-GAN) was proposed in order to address the issue of poor diversity and low overall quality in the image generated by adversarial network. The conditional enhancement was used to calculate the mean and covariance matrix of the text feature vector, generating conditional variable to replace the original high-dimensional text feature and solve the sparsity problem. The coordinate attention mechanism was introduced into the residual block of the generator network to form a deep fusion module combined with the coordinate attention mechanism (CA-Block). The long-term dependency relationship of feature between channels can be captured while retaining the precise position of feature and enhancing the representation of the target object. The spatial reconstruction unit was introduced into the discriminator network to form a feature space reconstruction module (SRU-Block). Redundant feature was separated via weight assignment and reconstruction, enhancing the discriminator’s ability to represent feature. The model was tested and verified using the CUB-200, Oxford-102 Flowers and COCO dataset. The experimental results showed that the IS and FID index value of the proposed model (CAT-GAN) were the best compared with models such as StackGAN++, AttnGAN, DAE-GAN, DM-GAN, DT-GAN and DF-GAN. The IS index value reached 5.13, 4.10 and 31.81, and the FID index value reached 14.34, 16.76 and 26.36. The proposed model has better visualization effect, proving the effectiveness of the proposed method.