In　this　study,　high　strength　seawater　coral　aggregate　concrete　(SCAC)　with　a　compressive　strength　of　80　MPa　was　prepared,　and　seawater　coral　aggregate　reinforced　concrete　slab　(SCARCS)　was　designed　with　epoxy-coated　steel　bars.　Combining　with　the　AE　technology,　monotonic　flexural　static　tests　were　conducted　on　SCARCS　specimens　with　three　varying　reinforcement　ratios　(i.e.,　0.85%,　1.13%,　and　1.41%).　Results　showed　that　the　increasing　the　reinforcement　ratio　led　to　an　increase　in　peak　bearing　capacity　and　a　decrease　in　center　deflections　at　the　peak.　The　loading　process　can　be　divided　into　four　stages　based　on　the　varying　magnitudes　of　the　b-value　and　Ib-value.　The　primary　mode　of　failure　was　tensile　microcracks　at　various　stages,　and　the　ratio　of　shear　microcracks　reached　its　maximum　value　in　the　final　stage　of　loading.　Besides,　based　on　the　signal　intensity　analysis,　it　can　be　observed　that　the　scale　and　complexity　of　cracks　propagation　inside　the　SCARCS　specimen　increased　as　the　reinforcement　ratio　rose.　In　addition,　back　propagation　neural　network　(BPNN)　classification　model　optimized　by　deep　belief　network　(DBN)　algorithm　was　proposed　to　predict　the　damage　degree　of　SCARCS　during　loading,　and　the　average　prediction　accuracy　of　the　proposed　model　was　shown　to　be　over　89%.　The　classification　outcomes　demonstrated　that　with　an　increase　in　the　reinforcement　ratio　of　SCARCS,　the　proportion　of　AE　signals　in　Cluster　3　increased,　while　the　proportion　of　AE　signals　in　Cluster　2　dropped.　Notably,　both　the　average　frequency　and　energy　presented　an　ascending　trend.　The　findings　of　this　study　provided　valuable　insights　for　designing　and　assessing　the　safety　of　SCAC　structures　for　essential　island　and　reef　engineering　infrastructure.