How　to　effectively　utilize　inter-frame　redundancies　is　the　key　to　improve　the　accuracy　and　speed　of　video　super-resolution　reconstruction　methods.　Previous　methods　usually　process　every　frame　in　the　whole　video　in　the　same　way,　and　do　not　make　full　use　of　redundant　information　between　frames,　resulting　in　low　accuracy　or　long　reconstruction　time.　In　this　paper,　we　propose　the　idea　of　reconstructing　key　frames　and　non-key　frames　respectively,　and　give　a　video　super-resolution　reconstruction　method　based　on　deep　back　projection　and　motion　feature　fusion.　Key-frame　reconstruction　subnet　can　obtain　key　frame　features　and　reconstruction　results　with　high　accuracy.　For　non-key　frames,　key　frame　features　can　be　reused　by　fusing　them　and　motion　features,　so　as　to　obtain　accurate　non-key　frame　features　and　reconstruction　results　quickly.　Experiments　on　several　public　datasets　show　that　the　proposed　method　performs　better　than　the　state-of-the-art　methods,　and　has　good　robustness.　©　2021,　The　Author(s),　under　exclusive　licence　to　Springer　Science+Business　Media,　LLC　part　of　Springer　Nature.