In the process of image style transfer, the main target of the stylized image is blurred due to the blurring of the foreground and background boundaries in image reconstruction. Image style transfer algorithm with clear target edges is proposed. The deep matting neural network used to extract outline of the content image is merged with the style transfer network to form a transparent mask to constrain the style transfer process, highlighting the outline of the main target of the stylized image. By replacing the max-pooling layer in the transfer network, more image background information is retained, and the overall structure of the stylized image is refined. The network model parameters are reduced by replacing the larger convolution kernel in the transfer network, and the calculation process of style transfer is simplified. Then, the normalization of the conventional-convolutional layer realizes parameter sharing between similar style transfers, improving the speed of style transfer. The VGG-19 neural network as a feature extractor extracts feature maps from the input content and style images, constrains the transformation from the input image to the output image in local affine of color space, and merges the target mask on the RGB channel of the input image so that the main goal of stylized images is to achieve texture synthesis in mask constraints. Experimental results show that compared with the traditional method, this method has obvious boundaries in the foreground and background of the stylized image, and the content structure is preserved well.