Sparse Computed Tomography （CT） reconstruction can reduce patient radiation dosage and is of great significance for clinical diagnosis. In deep learning-based image reconstruction tasks， classic networks such as Uformer （U-shape Transformer）， Restormer （Restoration Transformer）， and AST （Adaptive Sparse Transformer） fail to consider multi-scale and directional information in images， neglecting the balance between local details and global structure， resulting in limited artifact suppression. To address this issue， a Multi-scale Attention Adaptive Fusion Transformer （MAAF-Transformer） network was proposed. In this network， a parallel attention fusion strategy was adopted， a Multi-Scale Channel Direction-aware Attention （MSCDA） module was combined to capture artifact features， and a Convolutional Adaptive Spatial Channel Attention （CASCA） module was used to adjust the weights dynamically. Then， effective information was filtered through a Gated Deep-convolutional Feedforward Network （GDFN）. Experimental results show that at 60 sparse angles， MAAF-Transformer achieves 0.714 1 dB higher Peak Signal-to-Noise Ratio （PSNR）， 0.33% higher Structural SIMilarity （SSIM）， and 7.76% lower Root Mean Square Error （RMSE） compared with classic Uformer， with excellent performance in terms of visual effects as well. It can be seen that MAAF-Transformer has higher sparse reconstruction accuracy and stronger artifact suppression capability.