Chest CT(computed tomography) imaging diagnosis is one of the main diagnostic methods for the coronavirus disease 2019(COVID-19). Deep learning technologies such as convolutional neural networks are widely used in medical image processing because of their powerful nonlinear modeling capabilities.A neural network and digital image processing technology is used to design a lightweight COVID-19 classification model based on intra-volume and inter-volume attention mechanisms. Based on this model, we developed a new COVID-19 intelligent diagnosis system with a set of diagnostic functions, lesion segmentation functions and lung and pixel distribution histogram functions. We collected CT images of lungs from 247 patients with COVID-19, 152 other patients with pneumonia and 92 healthy people from the People's Hospital of Wuhan University and made them as training data sets for network training. The experimental results show that the accuracy of our proposed method on the screening task and the degree grading task on the validation set reached 88.63% and 89.65%, respectively, and the average diagnosis time per person was shortened to 0.4 seconds in the algorithm module, which has important application value.