Video　anomaly　detection　is　a　challenging　task　because　of　the　uncertainty　of　abnormal　events.　The　current　method　based　on　predictive　frames　has　obtained　better　detection　results　compared　with　the　previous　reconstruction　or　hand-crafted　methods.　In　current　prediction　methods,　the　characteristics　considered　previously　are　only　of　a　single　scale,　and　the　time　constraint　information　is　not　fully　used.　In　our　work,　we　proposed　a　new　framework　structure　to　achieve　better　abnormality　detection　rate.　To　address　the　objects　of　different　scales　in　each　video　frame,　we　considered　extracting　the　characteristics　of　different　receptive　fields　to　encode　more　spatial　information.　At　the　same　time,　we　added　temporal　constraints　to　the　network　instead　of　using　time-consuming　optical　flow　information,　and　we　completed　the　memory　of　temporal　features　through　a　ConvGRU　module.　Furthermore,　while　distinguishing　abnormal　events,　we　also　considered　temporal　information　and　spatial　information　so　that　our　framework　could　fully　combine　spatio-temporal　information　to　correctly　distinguish　abnormal　events　from　normal　events.　We　obtained　excellent　results　on　three　datasets,　thus　demonstrating　the　effectiveness　of　our　method.　©　2020　Elsevier　B.V.