Abstract: Real-time analysis and prediction of flow field characteristics are essential for flight safety and necessitating in-flight data acquisition, an almost impossible task for traditional techniques such as temperature/pressure-sensitive paints. Flexible smart skins demonstrate great promise for acquiring in-flight data. Nevertheless, the multi-physical data collected are usually sparsely in distribution and limited in quantity, making it difficult to accurately identify transition and stall locations. To overcome this limitation, we introduce a novel method for rapidly reconstructing high-resolution flow fields around aircraft wings from sparse data. Leveraging the flow fields computed by CFL3D, a dual-branch attention fusion model is developed based on an encoder-decoder network. This model facilitates rapid prediction of 2D flow fields for 52 types of NACA airfoils under 156 different freestream conditions, achieving an average relative error of 3.68% and providing a substantial amount of high-fidelity training data for the reconstruction process. Furthermore, using flow fields around the M6 airfoil generated by the rapid prediction model, a flow field reconstruction model is developed based on a shallow neural network. This model fulfills rapid reconstruction of high-resolution flow fields with a relative error of 2.73%. This work establishes a valuable data foundation for the in-flight application of smart skins, enhancing real-time monitoring capabilities and improving flight safety through accurate flow field reconstructions and insights.