With the rapid development of style migration technology, the global style migration technology has basically taken shape, but in the actual application process, there are problems such as the local style migration of the target area of the picture. Aiming at the above problems, this paper combines the residual network based on the convolutional neural network, and proposes a local style migration method based on residual neural network. Firstly, the mask is used to segment the content map to extract the target region. Secondly, the convolutional neural network extracts the image features and performs feature fusion. Then, the residual network is used to speed up the formation of the graph. Finally, the deconvolution is generated. A picture that only completes the style transition for the target area. In this paper, the several experiments are designed on the Microsoft Coco2014 dataset. The experimental results show that the local style migration network model based on residual neural network has better local style conversion ability and higher execution efficiency.