Due to the lack of cooperative information, non-cooperative spacecraft cannot obtain pose data directly from sensors. Therefore, a pose recognition network based on inverse synthetic aperture radar (ISAR) images is proposed. Compared with the images taken by space photography satellites and simulation data, this kind of image is easier to obtain and cheaper, but there are some problems such as low resolution ratio and incomplete panel image. Therefore, in image preprocessing, the network uses YOLOX-tiny as a spacecraft clipping network by adjusting it to avoid the data marked in the image affecting the subsequent network training, so that the network only focuses on the region where the spacecraft is located. The enhanced Lee filter is used to remove image noise and improve image quality. In the backbone network, the STN module is added to make the network select the most relevant region attention, and the U-Net is designed into a dense residual block structure and combined with the CBAM module to reduce the feature loss during sampling and improve the accuracy of the model. In addition, multi-head self-attention is introduced to capture more global information. The experimental results show that the minimum, maximum, and average errors of this model are improved compared with some mainstream models, and the errors are reduced by 0.5–0.6. All this proves that the network has a better pose recognition ability.