The aerodynamic shape of the bluff body section is very important to the aerodynamic performance. However, it takes a lot of time to obtain the aerodynamic performance of the bluff body section using traditional wind tunnel tests and CFD simulation calculations, which greatly affects the aerodynamic performance evaluation efficiency of the bluff body section , s aerodynamic shape. This paper proposes to use the deep learning technology of convolutional neural networks to realize the rapid prediction of aerodynamic performance. After the deep learning model is trained, the shape information and the shape-related flow field information can be input to output the drag coefficients under different geometric shapes, then the aerodynamic performance of the bluff body section. However to find the best deep learning model, this paper optimizes the depth and width of the convolutional neural network structure through comprehensive judgment error and time performance. The output resistance coefficient of the deep learning model is compared with the CFD calculation results. It is found that the error meets the expected requirements, and the prediction time based on the deep learning network is an order of magnitude improvement compared with the calculation time required by the traditional method. It can be used as the bluff body section aerodynamic shape optimization in the future.