There are too few samples in the dataset for the chest X-ray image classification task for COVID-19 and the existing two-stage classifier and three-stage classifier models are poor for image feature extraction at high latitudes and the model training is slow. To solve the above problems an improved classification task algorithm ConvNeXt-AT based on the ConvNeXt convolutional neural network was proposed. Firstly the ConvNeXt-AT classification model improved the image feature extraction ability by adding the mixed domain attention mechanism CBAM to the ConvNeXt Block layer. It not only considered the information interaction ability between channels but also the connection between pixels on the spatial domain. The ConvNeXt-AT model was obtained. Then the total variation regularization method was used to denoise the data set for the common Poisson noise in X-ray images. Finally a comparison experiment was conducted with 21 165 images from a large data set made public during the COVID-19 pandemic. The results show that in the case of sufficient training datasets the improved ConvNext-AT model improved the accuracy rates by 2% 2. 7% 2. 1% and 1. 9% over ResNet-50 MobileNet EfficientNet and the original ConvNext-T. The improved method has been proved to be feasible and the model has good robustness through the image visualization method of Grad-CAM to display the class activation graph.