The　non-local　attention　has　attracted　widespread　attention　from　researchers　in　enhancing　the　ability　of　deep　single-image　super-resolution　methods　to　mine　self-similarity　information.　However,　non-local　attention　often　faces　problems　of　high　computational　complexity　and　inaccurate　calculation　of　cross-correlation　of　deep　features　when　capturing　long-distance　information.　To　solve　these　problems,　we　propose　an　innovative　and　efficient　Weighted　Adaptive　Clustering　Attention　(WACA).　Specifically,　WACA　consists　of　Sparse　Adaptive　Clustering　Attention　(SACA)　and　Weighted　Residual　Attention　(WRA).　SACA　significantly　reduces　the　noise　signal　in　the　process　of　feature　correlation　calculation　with　the　help　of　asymmetric　local　sensitive　hashing,　and　reduces　the　computational　cost　from　quadratic　to　asymptotically　linear　with　the　sequence　length.　In　addition,　our　designed　WRA　utilizes　the　high　correlation　characteristics　of　the　self-similarity　matrix　between　self-attention　layers,　and　further　significantly　reduces　the　computational　consumption　of　associated　non-local　information　by　co-optimizing　the　self-similarity　matrix.　To　verify　the　effectiveness　of　WACA,　we　introduce　corresponding　modules　on　a　residual　backbone　and　construct　a　framework　named　Weighted　Adaptive　Clustering　Network　(WACN).　Experimental　results　demonstrate　that　WACN　has　competitive　performance　in　both　quantitative　and　qualitative　evaluations.