The total variation (TV) minimization algorithm is widely applied for handling sparse or noisy projection data in computed tomography (CT), to enable high-precision CT image reconstruction. However, traditional TV regularization terms suffer from issues of isotropy and single directionality, which limit improvements in the quality of reconstructed images. To address this problem, this paper proposes a sparse angle CT image reconstruction algorithm based on multi-directional total variation. This method incorporates information from multiple directions and adjusts the regularization factor, to better preserve the structural characteristics of the reconstructed image. Experiments were conducted using the Shepp-Logan phantom model and gear CT projection data, with peak signal-to-noise ratio, root mean-square error, and structural similarity used as evaluation criteria for reconstruction image quality. The results were compared with those of three other traditional reconstruction algorithms. The experimental results show that the images reconstructed by the proposed algorithm are closer to the original images and superior in detail preservation compared to those of the other three algorithms.