Dunhuang murals are dazzling treasures in the history of human world civilization. However, existing algorithmic studies on Dunhuang murals mainly focus on mural restoration, seldom concentrating on color style transfer. Therefore, a style transfer method for Dunhuang murals which incorporates the CBAM attention mechanism based on recurrent generative adversarial network is proposed in this study. By extracting the features of the input image and feeding them into the generator which is added with the CBAM attention mechanism, the attention mechanism is applied to improve the style transfer effect of the focus area and suppress the generation of boundary artifacts. To better retain the structural information of the image content, a residual network module is added between the down-sampling region and the up-sampling region. In addition, a color loss is added to the loss function to improve the stylization effect of the generated image by constraining the model. Experiments conducted on the self-constructed Dunhuang mural dataset validate the superiority over existing methods of the proposed model in the task of Dunhuang mural art style transfer. This model can generate stylized images of Dunhuang murals with more excellent visual effects and stronger artistic flavor, providing a new idea for innovative research on Dunhuang murals.