In wide angle inverse synthetic aperture radar (ISAR) imaging, serious migration through range cells (MTRC) will lead to the defocus of ISAR image. A wide angle ISAR imaging method based on U-net convolutional neural network (U-net CNN) is proposed, Firstly, the echo data is preprocessed by fast Fourier transform to obtain a defocused ISAR complex value image as the training samples; Secondly, according to ISAR imaging characteristics, the u-net structure is improved, and an imaging network with good focusing ability is obtained after training. Simulation results show that compared with traditional wide angle ISAR imaging methods, the proposed method reduces the peak sidelobe ratio (PSLR) of ISAR image to less than 18 dB, has smaller image entropy and minimum mean square error (NMSE), and the imaging time is reduced to about 0.28 seconds. Under the condition of low signal to noise ratio (SNR), the proposed method can still achieve fast and accurate reconstruction of ISAR image.