To address the problem of high false negative rate of RT - PCR in screening patients with COVID- 19 ,this paper proposes a DRPL-ViT computer - aided diagnostic network. The knowledge distillation mechanism is first introduced on the basis of Vision Transformer which enables the Transformer structure to be trained on small data sets to achieve better fitting re- sults. Then ,the dependencies between tokens can be better captured by encoding the position information of patches in a relative position encoding way that is more suitable for vision tasks. In order to focus on more local features a traditional convolution module is introduced in the Transformer Encoder module to extract local features. The experiments achieved an average classification accura- cy of 92. 11% on the four classification test sets and 97. 85% for COVID- 19. The experimental results indicate that the proposed network has a high accuracy in classifying neo - coronary pneumonia and other lung lesions ,and has some clinical application value.