Single　image　super-resolution　(SISR)　has　been　revolutionized　by　convolutional　neural　networks　(CNN).　However,　existing　SISR　algorithms　have　feature　extraction　and　adaptive　adjustment　limitations,　leading　to　information　duplication　and　unsatisfactory　image　reconstruction.　In　this　paper,　we　propose　a　deep　and　adaptive　feature　extraction　attention　network　(DAAN),　which　first　fully　extracts　shallow　features　and　then　adaptively　captures　precise　and　fine-scale　features　by　a　deep　feature　extraction　block　(DFEB).　It　includes　multi-dimensional　feature　extraction　blocks　(MFEBs)　that　combine　large　kernel　and　dynamic　convolution　layers　to　improve　large-scale　information　utilization　effectively.　Finally,　an　enhanced　spatial　attention　block　(ESAB)　to　further　selectively　reinforce　the　transmission　of　details.　A　large　number　of　experimental　results　show　that　our　proposed　model　reconstruction　performance　is　superior　to　existing　classical　methods.　This　paper　shows　the　architecture　of　the　proposed　deep　and　adaptive　feature　extraction　attention　network　(DAAN).　SFEB,　DFEB,　and　ESAB　stand　for　the　shallow　feature　extraction　block,　the　deep　feature　extraction　block,　and　the　enhanced　spatial　attention　block,　respectively.image