Aiming at the problem that the existing methods can only extract the low-order statistics of image features when implementing the task of style transfer, this study considered modeling the style transfer process as a feature distribution matching process, proposed a discriminator network based on Wasserstein distance, and defined a style loss function. Wasserstein discriminator can better fit the Wasserstein distance between feature distributions, and the defined style loss can better distinguish the difference between higher-order statistical information of image features. At the same time, in order to achieve the effect of real-time generation, a style transfer conversion module based on an encoder-decoder structure and an attention mechanism was introduced as a generation network. This generation network can effectively integrate the original image features and generate them. Specifically, the style loss was computed by adding a Wasserstein discriminator after the convolutional layer of the computational loss module. The training of the generative network was then supervised by the style loss together with the content loss calculated as the mean square error in the traditional methods, and after the network training, images can be input for the style transfer test. Finally, the network was trained on the benchmark MSCOCO and WikiArt datasets and the results were tested. The qualitative and quantitative experimental results showed that the proposed method can achieve real-time style transfer and generate high-quality stylization results compared with existing methods.