Due　to　the　complex　underwater　imaging　environment,　existing　Underwater　Image　Enhancement　(UIE)　techniques　are　unable　to　handle　the　increasing　demand　for　high-quality　underwater　content　in　broadcasting　systems.　Thus,　a　robust　quality　assessment　method　is　highly　expected　to　effectively　compare　the　quality　of　different　enhanced　underwater　images.　To　this　end,　we　propose　a　novel　quality　assessment　method　for　enhanced　underwater　images　by　utilizing　multiple　levels　of　features　at　various　stages　of　the　network＇s　depth.　We　first　select　underwater　images　with　different　distortions　to　analyze　the　characteristics　of　different　UIE　results　at　various　feature　levels.　We　found　that　low-level　features　are　more　sensitive　to　color　information,　while　mid-level　features　are　more　indicative　of　structural　differences.　Based　on　this,　a　Channel-Spatial-Pixel　Attention　Module　(CSPAM)　is　designed　for　low-level　perception　to　capture　color　characteristics,　utilizing　channel,　spatial,　and　pixel　dimensions.　To　capture　structural　variations,　a　Parallel　Structural　Perception　Module　(PSPM)　with　convolutional　kernels　of　different　scales　is　introduced　for　mid-level　perception.　For　high-level　perception,　due　to　the　accumulation　of　noise,　an　Adaptive　Weighted　Downsampling　(AWD)　layer　is　employed　to　restore　the　semantic　information.　Furthermore,　a　new　top-down　multi-level　feature　fusion　method　is　designed.　Information　from　different　levels　is　integrated　through　a　Selective　Feature　Fusion　(SFF)　mechanism,　which　produces　semantically　rich　features　and　enhances　the　model＇s　feature　representation　capability.　Experimental　results　demonstrate　the　superior　performance　of　the　proposed　method　over　the　competing　image　quality　evaluation　methods.