Abstract: The deep-learning-based super-resolution reconstruction methods developed in recent years are effective methods to obtain detailed flow fields. A deep learning-based super-resolution reconstruction method was applied to reconstructing high-resolution wind field of flow around building structures in this paper. The super-resolution reconstruction model was based on the convolutional neural network (CNN) and combined with the mixed downsampled skip-connection multi-scale (Multi-scale CNN) model. The super-resolution reconstruction model was applied to the reconstruction of the surface pressure on and the velocity field around the CAARC benchmark model. The reconstruction ability of the deep learning-based model for different under-resolution flow fields was investigated. The results show that the proposed deep learning model can greatly enhance the super-resolution reconstruction performance and the reconstruction accuracy is better than the original convolutional neural network model and the traditional bicubic interpolation method. Due to its universal applicability, this method can be extended to super-resolution reconstruction of wind field of any building structure with complex turbulent flow.