Microwave correlation imaging extends from the idea of quantum intensity correlation imaging to the microwave field, and this not only solves the problems of highresolution staring imaging and complex motion compensation in traditional radars, but also has the characteristics of high resolution and strong antiinterference ability, and extensive attention. In view of the high sampling number and poor reconstruction quality of traditional microwave correlation imaging reconstruction algorithms, this paper proposes a reconstruction method of microwave correlation imaging based on convolutional neural network and residual network. The radar receiver echo data are used as the input of the network, after the initial reconstruction, the trained feature extraction network and the image enhancement network are used for feature extraction and feature enhancement in order to perform image reconstruction. The algorithm in this paper is compared with pseudoinverse algorithm and compressed sensing algorithm. The simulation results show that compared with the existing optimal microwave correlation reconstruction compressed sensing algorithm, the image reconstructed by this algorithm has more 〖JP2〗advantages. At the same time and without sacrificing image quality, the expending time to execute a single image reconstruction program is about 0.06 s,〖JP〗 improving the speed of image reconstruction and having great significance to engineering applications.