Manually　designed　deep　neural　networks　have　successfully　forwarded　waste　recognition　tasks　in　the　resource　recycling　field.　However,　due　to　the　diversity　of　waste　samples,　the　feature　extraction　ability　of　inherently　designed　models　fails　to　fully　satisfy　the　requirements　of　real　world　applications.　In　this　article,　an　attention-aware　based　differentiable　architecture　search　(ADAS)　network　for　waste　recognition　is　proposed,　which　self-organizes　to　generate　an　optimal　network　structure　according　to　diverse　waste　data.　First,　a　structured　search　space　with　attention-aware　modules　is　designed　to　enhance　the　diverse　feature　representations　of　waste　data.　Secondly,　an　efficient　and　differentiable　structure　search　is　achieved　by　continuously　relaxing　the　representation　of　the　network　architecture　and　search　space.　Finally,　the　optimal　architecture　search　process　is　evaluated　by　a　bi-level　optimization　algorithm.　Experimental　results　show　that　the　proposed　method　achieves　more　satisfactory　classification　performances　than　the　manually　designed　ResNet,　DenseNet　networks　in　the　TrashNet　dataset　and　the　self-built　household　waste　dataset.　©　2023　IEEE.