Concrete　collapse　is　a　quantitative　indicator　of　the　degree　of　fluidity　of　the　mix,　which　is　used　to　determine　whether　the　construction　can　be　carried　out　properly.　During　the　whole　cycle　of　concrete,　it　needs　to　go　through　three　processes:　preparation,　transportation　and　pouring.　In　this　paper,　we　focus　on　the　transportation　process　of　concrete　and　use　computer　vision　algorithms　with　deep　learning　convolutional　neural　networks　to　determine　the　collapse　value　of　freshly　mixed　concrete　inside　a　mixer　truck.　To　address　the　problem　of　how　to　determine　concrete　collapse　easily,　quickly　and　accurately,　and　to　improve　the　efficiency　of　concrete　collapse　determination,　this　paper　proposes　a　new　structural　model　of　AWFMFNet　image　classification　network　for　concrete　collapse.　The　network　model　designed　in　this　paper　enhances　feature　extraction　and　fusion　by　adaptive　weighting　algorithm　and　pixel-level　feature　enhancement　algorithm,　which　enables　the　network　model　to　further　extract　key　features　and　fuse　deep　and　shallow　image　features　to　better　guide　the　image　classifier　to　make　final　classification　prediction.　This　paper　conducts　experiments　using　concrete　collapse　datasets　collected　from　real　application　scenarios　and　compares　them　with　several　other　classical　image　classification　network　models.　The　experimental　results　show　that　the　network　model　can　effectively　extract　the　multilayer　features　of　concrete　images　and　has　better　collapse　classification　results.　The　network　model　improves　the　accuracy　by　4.62%　compared　with　ResNet50,　3.27%　compared　with　DarkNet53,　1.74%　compared　with　GoogLeNet,　and　7.21%　compared　with　VGG16.　©　2022　IEEE.