The U-Former model is proposed in this work to segment the COVID-19 lung computed tomography images of patients. U-Former introduces the transformer architecture, based on the traditional U-Net segmentation network, which effectively improves the network’s ability to capture global features. The mixed module is presented in this work to capture long-range dependencies and extract local information. In the mixed module, the computationally expensive self-attention mechanism is enhanced and combined with convolution to enable the network to efficiently capture global information while taking into account local details. The multi-scale attention module is utilized to fuse the multi-scale features to enhance the segmentation effect for details. Experimental results show that the proposed U-Former model outperforms other state-of-the-art segmentation models, including both convolutional neural network-based and transformer-based models, with a mean Dice score of 82.54%, a mean intersection over union of 80.01%, and a mean sensitivity of 85.70%. The code and models are publicly available at https://github.com/tianyuzhou668/U-Former