Community　green　spaces　(CGSs)　constitute　a　crucial　element　of　urban　land　use,　playing　a　pivotal　role　in　maintaining　the　stability　of　urban　ecosystems　and　enhancing　the　overall　quality　of　the　urban　environment.　Through　the　post-occupancy　evaluation　(POE)　of　green　spaces,　we　can　gain　insights　into　residents＇　actual　needs　and　usage　habits,　providing　scientific　evidence　for　the　planning,　design,　and　management　of　green　spaces.　This　ensures　that　CGSs　better　meet　residents＇　needs　and　improve　their　quality　of　life.　The　POE　of　CGSs　relies　heavily　on　high-precision　data　support.　However,　the　current　POE　system　for　CGSs　faces　challenges,　such　as　limited　data　collection　methods,　incomplete　indicator　systems,　and　excessive　manual　involvement.　To　address　these　limitations　in　data　collection,　this　study　proposes　a　comprehensive,　dynamically　monitored,　objective,　and　sustainable　POE　system　for　CGSs.　This　system　incorporates　a　multi-dimensional　perception　system　that　integrates　the　Internet　of　Things　(IoT)　and　sensors　to　collect　data　from　various　sources.　It　establishes　an　evaluation　framework　from　the　perspectives　of　policy　guidance　and　usage　needs　for　CGSs,　utilizing　neural　network　systems　and　artificial　intelligence　techniques　to　compute　the　evaluation　results.　Using　the　Jiayuan　Sanli　Community　in　Beijing　as　a　case　study,　this　paper　demonstrates　the　feasibility　of　the　proposed　system.　A　comparison　between　the　POE　results　obtained　using　the　multi-dimensional　perception　technique　and　those　obtained　manually　reveals　an　87%　improvement　in　the　accuracy　of　the　evaluation　results　based　on　the　multi-dimensional　perception　system.　This　system　bridges　the　gap　between　planning　perspectives　and　user　experiences,　contributing　significantly　to　future　urban　land　planning　and　land　policy　formulation.