In　light　curves　(LCs),　the　brightness　of　stars　is　associated　with　time,　and　so　is　its　image.　The　traditional　data　processing　methods　cannot　effectively　handle　real-time　and　large-volume　data　of　various　LCs.　To　address　this　issue,　this　work　develops　a　deep　neural　network　named　Dropout-based　Recurrent　Neural　Networks　(DRNN).　It　extracts　complicated　features　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.　DRNN　can　also　produce　warnings　for　abnormal　values　of　light　change　curves.　Furthermore,　this　work　optimizes　the　training　model　by　combining　a　dropout　method　with　an　adaptive　moment　estimation　algorithm　to　iteratively　update　the　network　weight　of　the　RNN　based　on　the　LCs　data.　Extensive　experiments　with　a　Mini-GWAC　dataset　demonstrate　that　DRNN　outperforms　several　typical　methods　in　terms　of　prediction　performance　of　star　brightness　in　large-scale　astronomical　LCs.　©　2020　IEEE.