Ground-based　Wide-angle　Camera　array　(GWAC)　is　a　short-time　survey　telescope　which　can　produce　images　every　15　seconds　for　more　than　30,000　stars.　Light　curve　is　generated　from　star　image　with　a　series　of　processing.　Research　on　light　curve　is　a　new　task　in　time　domain　astronomy　which　can　detect　anomaly　astronomical　events.　We　explore　a　series　prediction　model　of　LSTM　neural　network　for　light　curve　prediction.　Through　model　training　and　validation　we　obtain　the　optimal　structure.　Then　we　predict　one　time-step　ahead　light　luminance　for　test　star.　We　evaluate　the　performance　of　model　by　calculating　prediction　error.　Anomaly　detection　mechanism　is　based　on　prediction　error.　Experimental　results　based　on　real　light　curve　data　demonstrates　that　our　model　is　promising　in　light　curve　prediction　and　anomaly　detection.　©　2018　Published　under　licence　by　IOP　Publishing　Ltd.