In　light　curves,　the　brightness　of　stars　is　associated　with　time,　and　it　is　an　image　of　the　brightness　with　respect　to　time.　The　traditional　data　processing　methods　cannot　effectively　handle　real-time　and　large-volume　data　of　various　light　curves.　To　address　this　issue,　this　work　develops　a　deep　neural　network,　named　Dropout　Recurrent　Neural　Networks　(DRNN).　It　extracts　complicated　characteristics　of　all　images　captured　by　Mini　Ground-based　Wide-Angle　Camera　array　(Mini-GWAC)　for　point　source　extraction　and　cross-certification　through　Long　Short-Term　Memory　units.　Furthermore,　this　work　optimizes　the　training　model　by　combining　a　dropout　method,　which　predicts　changes　of　the　star　brightness　in　advance.　Extensive　experiments　with　Mini-GWAC　dataset　demonstrate　that　DRNN　outperforms　several　typical　baseline　methods　with　respective　to　forecasting　performance　of　star　brightness　in　large-scale　astronomical　light　curves.