We propose BraggNN, a deep-learning based method, to accelerate the most computation-intensive part of polycrystal diffraction data analysis (diffraction signal characterization). The application of BraggNN for real experimental data demonstrates that it can deliver consistent (sometimes even slightly better) results compared with the conventional method while running hundreds of times faster.