Structured　clustering　networks,　which　alleviate　the　oversmoothing　issue　by　delivering　hidden　features　from　autoencoder　(AE)　to　graph　convolutional　networks　(GCNs),　involve　two　shortcomings　for　the　clustering　task.　For　one　thing,　they　used　vanilla　structure　to　learn　clustering　representations　without　considering　feature　and　structure　corruption;　for　another　thing,　they　exhibit　network　degradation　and　vanishing　gradient　issues　after　stacking　multilayer　GCNs.　In　this　article,　we　propose　a　clustering　method　called　dual-masked　deep　structural　clustering　network　(DMDSC)　with　adaptive　bidirectional　information　delivery　(ABID).　Specifically,　DMDSC　enables　generative　self-supervised　learning　to　mine　deeper　interstructure　and　interfeature　correlations　by　simultaneously　reconstructing　corrupted　structures　and　features.　Furthermore,　DMDSC　develops　an　ABID　module　to　establish　an　information　transfer　channel　between　each　pairwise　layer　of　AE　and　GCNs　to　alleviate　the　oversmoothing　and　vanishing　gradient　problems.　Numerous　experiments　on　six　benchmark　datasets　have　shown　that　the　proposed　DMDSC　outperforms　the　most　advanced　deep　clustering　algorithms.