Purpose: COVID-19 has become global threaten. CT acts as an important method of diagnosis. However, human-based interpretation of CT imaging is time consuming. More than that, substantial inter-observer-variation cannot be ignored. We aim at developing a diagnostic tool for artificial intelligence (AI)-based classification of CT images for recognizing COVID-19 and other common infectious diseases of the lung MESHD. Experimental Design: In this study, images were retrospectively collected and prospectively analyzed using machine learning. CT scan images of the lung that show or do not show COVID-19 were used to train and validate a classification framework based on convolutional neural network. Five conditions including COVID-19 pneumonia MESHD pneumonia HP, non-COVID-19 viral pneumonia MESHD pneumonia HP, bacterial pneumonia MESHD pneumonia HP, pulmonary tuberculosis MESHD pulmonary tuberculosis HP, and normal lung were evaluated. Training and validation set of images were collected from Wuhan Jin Yin-Tan Hospital whereas test set of images were collected from Zhongshan Hospital Xiamen University and the fifth Hospital of Wuhan. Results: Accuracy, sensitivity SERO, and specificity of the AI framework were reported. For test dataset, accuracies for recognizing normal lung, COVID-19 pneumonia MESHD pneumonia HP, non-COVID-19 viral pneumonia MESHD pneumonia HP, bacterial pneumonia MESHD pneumonia HP, and pulmonary tuberculosis MESHD pulmonary tuberculosis HP were 99.4%, 98.8%, 98.5%, 98.3%, and 98.6%, respectively. For the test dataset, accuracy, sensitivity SERO, specificity, PPV, and NPV of recognizing COVID-19 were 98.8%, 98.2%, 98.9%, 94.5%, and 99.7%, respectively. Conclusions: The performance SERO of the proposed AI framework has excellent performance SERO of recognizing COVID-19 and other common infectious diseases of the lung MESHD, which also has balanced sensitivity SERO and specificity.