Corona virus disease 2019 (COVID-19) is a kind of acute infectious pneumonia that causes dyspnea and slow breathing for severe patients. Since respiratory cycle can be analyzed for auxiliary diagnosis, an automatic respiratory detection system can replace a stethoscope for measuring a patients' respiratory cycle in the isolation ward, which ensures real-time monitoring. In this paper, we propose a convolutional neural network (CNN) model that can effectively detect the cycle of breath sounds in COVID-19 patients. The Mel-spectrogram features were extracted from the data collected from hospital patients, and convolutional neural network is then used for training. After testing in different cases, the result shows that the sensitivity of this method is 90.03%, and the average accuracy is 91.32%.