Generative Adversarial Network (GAN) can generate images that are close to real images, thus plays an important role in image style transfer.However, the GAN-based image transfer is perplexed by problems of low quality of generated images and difficult training of models, herein a new style transfer approach based on BicycleGAN model is proposed.First, the residual module is introduced into the generator of GAN to solve the degradation of GAN in training, and the self-attention mechanism is employed to obtain more image features thus improve the generation quality of the generator.To solve the gradient explosion in the training of GAN, the spectral normalization is added behind each convolution layer of the discriminator.Then the perceptual loss is introduced to address the unstable training and low generated image quality.The experiments on Facades and AerialPhoto&Map datasets show that the proposed approach outperforms other image style transfer methods in the PSNR and SSIM values of the generated images.