Considering　that　existing　recurrent　neural　network-based　video　deblurring　methods　are　limited　in　cross-frame　feature　aggregation　and　computational　efficiency,　an　efficient　spatio-temporal　feature　extraction　recurrent　neural　network　is　proposed.　Firstly,　we　combine　a　residual　dense　module　with　the　channel　attention　mechanism　to　efficiently　extract　discriminative　features　from　each　frame　of　a　given　sequence.　Then,　a　spatio-temporal　feature　enhancement　and　fusion　module　is　proposed　to　select　features　from　the　highly　redundant　and　interfering　sequential　features　and　integrate　them　into　the　features　of　the　current　frame.　Finally,　the　enhanced　features　of　the　current　frame　are　converted　into　the　deblurred　image　by　a　reconstruction　module.　The　quantitative　and　qualitative　experimental　results　on　three　public　datasets,　containing　both　synthetic　and　real　blurred　videos,　show　that　the　proposed　network　can　achieve　excellent　video　deblurring　effect　with　less　computational　cost.　Among　them,　on　the　GOPRO　dataset,　the　PSNR　reaches　31.43dB　and　the　SSIM　reaches　0.9201.　©　2023　Institute　of　Computing　Technology.　All　rights　reserved.