  Beam dump experiments provide a distinctive opportunity to search for dark
photons, which are compelling candidates for dark matter with low mass. In this
study, we propose the application of Graph Neural Networks (GNN) in tracking
reconstruction with beam dump experiments to obtain high resolution in both
tracking and vertex reconstruction. Our findings demonstrate that in a typical
3-track scenario with the visible decay mode, the GNN approach significantly
outperforms the traditional approach, improving the 3-track reconstruction
efficiency by up to 88% in the low mass region. Furthermore, we show that
improving the minimal vertex detection distance significantly impacts the
signal sensitivity in dark photon searches with the visible decay mode. By
reducing the minimal vertex distance from 5 mm to 0.1 mm, the exclusion upper
limit on the dark photon mass ($m_A\prime$) can be improved by up to a factor
of 3.
