Abstract: Whether it is based on numerical simulation or physical experiments, the acquisition of high-precision flow field data is not only extremely limited, but also often accompanied by high costs. Existing methods cannot reconstruct a more refined flow field structure with limited data, which greatly restricts the design accuracy and design efficiency of related gas/hydrodynamic engineering problems. The proposed physics-informed neural network (PINN) framework can make the dilemma solved to a certain extent for the traditional data-driven neural network cannot deal with the sparse problem. In this paper, a sparse data-driven flow field reconstruction method was developed based on the PINN framework. Firstly, coupling physical information with the neural network and utilizing a small amount of data for training, the method was arranged to be able to output complete flow field data. Then, the prediction error mechanism was revealed, and the prediction ability of the method for different structural flow fields was discussed. The results show that, using only extremely limited flow field data, coupling NS equation, the physical information-driven neural network can achieve high-precision reconstruction of the entire flow field, and the vortex structure of the convective field can also be captured accurately.