In　recent　years,　with　the　continuous　development　of　deep　learning　technology,　various　semantic　segmentation　algorithms　based　on　deep　learning　have　emerged,　but　most　of　the　segmentation　algorithms　cannot　achieve　high　speed　and　high　accuracy　at　the　same　time,　and　a　real-time　semantic　segmentation　framework　for　multi-channel　depth-weighted　aggregation　networks　(MCDWA_Net)　is　proposed　to　solve　this　problem.　Firstly,　the　multi-channel　idea　is　introduced　to　construct　a　three-channel　semantic　representation　model,　which　is　used　to　extract　three　types　of　complementary　semantic　information　of　the　image:　1)　Low-level　semantic　channel　outputs　the　local　features　such　as　the　edge,　color,　and　structure　of　the　object　in　the　image;　2)　Auxiliary　semantic　channel　extracts　the　transition　information　between　low-level　semantics　and　high-level　semantics,　and　realizes　multi-layer　feedback　to　the　high-level　semantic　channel;　3)　Advanced　semantic　channel　obtains　context　logical　relationships　and　category　semantic　information　in　images.　Then,　a　three-class　semantic　feature　weighted　aggregation　module　is　designed　to　output　a　more　complete　global　semantic　description.　Finally,　an　enhancement　training　mechanism　is　introduced　to　realize　the　feature　enhancement　in　the　training　stage,　thereby　improving　the　training　speed.　Experimental　results　show　that　the　proposed　method　not　only　has　fast　inference　speed,　but　also　has　high　segmentation　accuracy　in　complex　scenes,　which　can　achieve　the　balance　of　semantic　segmentation　speed　and　accuracy.　©　2024　Northeast　University.　All　rights　reserved.