Video　super-resolution　reconstruction　methods　based　on　deep　learning　are　often　faced　with　the　problems　of　long　time　consumption　or　low　accuracy.　A　video　super-resolution　reconstruction　method　based　on　deep　residual　network　is　proposed.　It　reconstructs　videos　with　high　accuracy　quickly　and　meets　the　real-time　requirements　for　low-resolution　videos.　Firstly,　the　adaptive　key　frame　discrimination　subnet　is　utilized　to　adaptively　identify　key　frames　from　the　video.　Then,　the　reconstruction　results　of　the　key　frames　are　obtained　by　the　high　precision　reconstruction　subnet.　For　non-key　frames,　the　reconstruction　results　are　directly　gained　based　on　the　features　obtained　by　fusing　the　features　of　the　corresponding　key　frame　and　the　motion　estimation　features　between　the　non-key　frame　and　the　adjacent　key　frame.　Experiments　on　open　datasets　show　that　videos　are　fast　reconstructed　by　the　proposed　method　with　high　accuracy　and　robustness.