To　address　the　challenges　of　high　model　complexity　and　excessive　parameter　counts　in　existing　image　super-resolution　(SR)　reconstruction　methods,　a　lightweight　image　SR　reconstruction　method　based　on　multi-scale　spatial　adaptive　attention　network(MSAAN)　is　proposed.　First,　a　global　feature　modulation　module(GFM)　is　designed　to　learn　global　texture　features.　Additionally,　a　lightweight　multi-scale　feature　aggregation　module(MFA)　is　introduced　to　adaptively　aggregate　high-frequency　spatial　features　from　local　to　global　scales.　Second,　the　multi-scale　spatial　adaptive　attention　module(MSAA)　is　proposed　by　integrating　GFM　and　MFA.　Finally,　a　feature　interactive　gated　feed-forward　module(FIGFF)　is　incorporated　to　enhance　the　local　feature　extraction　capability　while　reducing　the　channel　redundancy.　Extensive　experiments　demonstrate　that　MSAAN　effectively　captures　more　comprehensive　and　refined　features,　significantly　improving　reconstruction　quality　while　maintaining　a　lightweight　structure.　©　2025　Science　Press.　All　rights　reserved.