To study the differences in imaging characteristics and prediction of COVID-19 and non-COVID-19 viral pneumonia through chest CT.Chest CT data of 128 cases of COVID-19 and 47 cases of non-COVID-19 viral pneumonia confirmed by several hospitals were retrospectively collected, the imaging performance was evaluated and recorded, different imaging features were statistically analyzed, and a prediction model and independent predicted imaging features were obtained by multivariable analysis.COVID-19 was more likely than non-COVID-19 pneumonia to have a high-grade ground glass opacities (P = .01), extensive lesion distribution (P < .001), mixed lesions of varying sizes (27.7% vs 57.0%, P = .001), subpleural prominence (23.4% vs 86.7%, P < .001), and lower lobe prominence (48.9% vs 82.0%, P < .001). However, peribronchial interstitial thickening was more likely to occur in non-COVID-19 viral pneumonia (36.2% vs 19.5%, P = .022). The statistically significant differences from multivariable analysis were the degree of ground glass opacities (P = .001), lesion distribution (P = .045), lesion size (P = .020), subpleural prominence (P < .001), and lower lobe prominence (P = .041). The sensitivity and specificity of the model were 94.5% and 76.6%, respectively, with an AUC of 0.91.The imaging characteristics of COVID-19 and non-COVID-19 viral pneumonia are different, and the prediction model can further improve the specificity of chest CT diagnosis.