Abstract: Pneumonia often lacks obvious respiratory symptoms, symptoms are more atypical, easy to occur missed diagnosis, misdiagnosis. Deep learning methods can assist medical personnel in diagnosing COVID-19 safely and efficiently. According to the characteristics of ground-glass opacity, crazy paving sign and vasodilatation in CT images of COVID-19 patients, a light weight network DL-CTNet is designed to effectively extract the local and global features in CT images. Firstly, after inputting the pre-processed CT images, the shallow features are extracted by two branches of cavity convolution and dynamic dual-path multi-scale feature fusion(D-DMFF). Then, the D-DMFF module in local and global feature concatenation module (LGFC) is used to extract local features, and Swin Transformer is used to extract global features, and the deep features are obtained by the above two branches in LGFC. Finally, the classification label is output by the fully connected layers. The low complexity and effectiveness of CTNet are verified on two CT image datasets. The experimental results show that the classification accuracy of DL-CTNet is as high as 98.613%, and compared with other methods, DL-CTNet can more accurately detect pneumonia CT images.