Accurate segmentation of COVID-19 pneumonia lesions on chest CT images can facilitate the diagnosis of pneumonia. The CT image finds of which contained the ground-glass opacity, consolidation, pleural effusion, etc. This study proposed a deep neural network RCB-UNet＋＋ for the segmentation of COVID-19 pneumonia lesions in CT images, which exhibit large variations in texture, size and location. The model was built on top of the UNet＋＋ network with an extra residual module and an attention module. This architecture is able to effectively extract low-level texture features and high-level semantic information, thus improving the model performance. The RCB-UNet＋＋ model was trained on 45 samples and tested by another 50 cases. Finally, it achieved a Dice coefficient of 0.715, a sensitivity and specificity of 0.754 and 0.952, outperforming other designed models on the same dataset. The results demonstrate that the proposed algorithm improves the segmentation performance and has potential in fully automatic segmentation of COVID-19 pneumonia lesions on CT images.