Object　detection　is　an　important　task　of　computer　vision　used　to　detect　instances　of　visual　objects　of　a　certain　class　in　digital　images.　Video　object　detection　aims　to　locate　single　or　multiple　objects　in　sequential　images　and　assign　category　labels　for　them.　There　are　similarities　between　video　object　detection　and　image　object　detection,　so　some　image　object　detection　methods　are　usually　used　for　video　object　detection.　However,　due　to　motion　blur,　occlusion,　morphological　diversity,　and　illumination　changes　in　video,　video　object　detection　algorithms　have　higher　requirements.　In　the　framework　of　video　object　detection　based　on　feature　reuse　and　recursive　fusion,　we　propose　a　multi-scale　learnable　sampling　alignment　(MLFA)　network　for　video　object　detection.　MLFA　divides　the　video　frame　into　the　key　frame　and　non-key　frame　and　propagates　a　memory　feature　containing　historical　key　frame　information　in　the　time　dimension　to　compensate　for　the　current　frame　feature　through　feature　fusion.　In　the　process　of　alignment,　the　feature　pyramid　is　first　established,　and　then　the　alignment　features　of　different　levels　are　learned　in　a　learnable　way.　After　that,　features　from　different　levels　are　fused　to　leverage　multi-scale　information.　MLFA　maintains　the　efficiency　and　further　improves　the　detection　accuracy.