Concerning the problem of streak artifacts generated in the sparse reconstruction of analytic method， a Channel Attention U-shaped Transformer （CA-Uformer） was proposed to achieve high-precision Computed Tomography （CT） sparse reconstruction. In CA-Uformer， channel attention and spatial attention in Transformer were fused， and with the dual-attention mechanism， image detail information was easier learnt by the network； an excellent U-shaped architecture was adopted to fuse multi-scale image information； a forward feedback network design was implemented by using convolutional operations， which further coupled the local information association ability of Convolutional Neural Network （CNN） and the global information capturing ability of Transformer. Experimental results show that CA-Uformer has the Peak Signal-to-Noise Ratio （PSNR）， Structural Similarity （SSIM） 3.27 dB and 3.14% higher， and Root Mean Square Error （RMSE） 35.29% lower than the classical U-Net， which is a significant improvement. It can be seen that CA-Uformer has sparse reconstruction with higher precision and better ability to suppress artifacts.