In $x$-ray CT (computed tomography) imaging technology, there often exist the detection objects with the special sizes, shapes or materials, where the projection data can are only collected in some limited projection angles. In this case, the obtained data does not meet CT accurate reconstruction conditions. The reconstructed CT images using the conventional algorithm show some serious landslide artifacts, which are difficult to provide valuable information for many practical applications. In order to better suppress the landslide artifacts, this paper proposes the limited-angle CT image reconstruction method based on the ResNet model, in which the used residual learning method can extract the features of the input images and capture enough detail information, and then the deconvolution algorithm is used to restore the learned features. The numerical experiments prove the effectiveness of the proposed reconstruction method, which can significantly reduce the landslide artifacts and effectively retain the structural features of CT images. The reconstruction results can provide high quality CT images for clinical diagnosis.