Abstract: Purpose: Utilizing deep learning techniques, this study aimed to develop an artificial intelligence model that automatically annotates lesion computed tomography (CT) data, accurately and rapidly distinguishing novel coronavirus pneumonia (COVID-19) from other community-acquired pneumonia cases. Methods: A retrospective analysis was conducted on data from 248 patients with COVID-19 and 347 patients with other types of pneumonia. The COVID-19 cases were differentiated from other pneumonia cases during classification. After performing artificial intelligence-based lung segmentation, the extracted abnormal CT image features were dimensionally reduced and inputted into various classical machine learning models, Three-dimensional convolutional neural network (3D CNN), and attention-Multiple-instance learning (MIL) deep neural network architectures. The diagnostic performance of the models was evaluated using metrics such as receiver operating characteristic (ROC) curves, Precision Recall (PR) curves, Area Under Curve (AUC), sensitivity, specificity, and accuracy. Results: Among the classical machine learning models, K-Nearest Neighbor (KNN)demonstrated good performance, with an AUC of 0.793, Average Precision (AP) of 0.886, Balanced F Score (F1-score) of 0.7608, accuracy of 0.7512, sensitivity of 0.7754, and precision of 0.7691 on the external test set. The classical 3D CNN model exhibited satisfactory performance on the external test set with an AUC of 0.635, AP of 0.816, F1-score of 0.7144, accuracy of 0.7783, sensitivity of 0.6603, and precision of 0.6200. The attention-MIL model showed better robustness on the external test set, achieving an AUC of 0.851, AP of 0.935, F1-score of 0.8193, accuracy of 0.9155, sensitivity of 0.7414, and precision of 0.7646. Conclusion: Compared to the radiomics-enhanced and 3D CNN models, the deep learning attention-MIL model exhibited better performance in the differential diagnosis of COVID-19 and other community-acquired pneumonia.