We present a novel DenseNet framework with attention mechanisms (AM-DenseNet) to extract lung feature of ¹ COVID-19 patient. In AM-DenseNet, a lightweight Efficient Channel Attention (ECA) structure is added at the end of each dense connection to introduce an attention mechanism to discovery local lesion domain. We compare our AM-DenseNet to VGG-16, ResNet-50 and DenseNet-121 on CT image dataset of COVID-19 patients. According to the experimental results, we conclude that the classification performance of AM-DensNet framework can be significantly enhanced under the effect of attention mechanism. The AM-DensNet shows better classification performance than the compared models.