In the real applications of computed tomography (CT) imaging, the projection data of the scanned objects are usually acquired within a limited-angle range because of the limitation of the scanning condition. Under these circumstances, conventional analytical algorithms, such as filtered back-projection (FBP), do not work because the projection data are incomplete. The regularization method has proven to be effective for tomographic reconstruction from under-sampled measurements. To deal with the limited-angle CT reconstruction problem, the regularization method is commonly used, but it is difficult to find a generic regularization term and choose the regularization parameters. Moreover, in some cases, the quality of reconstructed images is less than satisfactory. To solve this problem, we developed an alternating direction method of multipliers (ADMM)-based deep reconstruction (ADMMBDR) algorithm for limited-angle CT. First, we used the ADMM algorithm to decompose a regularization reconstruction model. Then, we utilized a deep convolutional neural network (CNN) to replace a part of the ADMM algorithm to reduce artifacts and avoid the choice of the regularization term and the regularization parameter. Furthermore, we conducted some numerical experiments to evaluate the feasibility and the advantages of the proposed algorithm. The results showed that the proposed algorithm had a better performance than several state-of-the-art algorithms; with respect to structure preservation and artifact reduction.