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This study aims to explore the feasibility and accuracy of using deep learning to assist doctors in the diagnosis of COVID-19 by chest X-ray. Firstly, a UNet segmentation model was trained using the open COVID-QU-Ex Dataset training set to realize the automatic segmentation of lung ROI region. Secondly, the automatic extraction preprocessing of the lung region of the public dataset is completed. Thirdly, a classification model MBCA-COVIDNET is trained by using the preprocessed three classification image data (COVID-19, other pneumonia and normal) in the way of transfer learning. This model takes MobileNetV2 as the backbone network and adds the coordinate attention mechanism (CA) to it. Finally, a COVID-19 intelligent auxiliary diagnosis system convenient for doctors is built by using the trained model and the open-source software of Hugging Face.The model achieved 97.98% accuracy on the COVID-QU-Ex Dataset test set, while the parameters and MACs of the model were only 2.23M and 0.33G, which was easy to deploy on hardware devices. Conclusion The intelligent diagnosis system can help doctors to diagnose COVID-19 based on chest radiographs, and improve the accuracy and efficiency of diagnosis.