Collecting　and　analyzing　data　from　all　devices　to　improve　the　efficiency　of　business　processes　is　an　important　task　of　Industrial　Internet　of　Things　(IIoT).　In　the　age　of　data　explosion,　extensive　text　data　generated　by　the　IIoT　have　given　birth　to　a　variety　of　text　representation　methods.　The　task　of　text　representation　is　to　convert　the　natural　language　to　a　form　that　computer　can　understand　with　retaining　the　original　semantics.　However,　these　methods　are　difficult　to　effectively　extract　the　semantic　features　among　words　and　distinguish　polysemy　in　natural　language.　Combining　the　advantages　of　convolutional　neural　network　(CNN)　and　variational　autoencoder　(VAE),　this　paper　proposes　an　intelligent　CNN-VAE　text　representation　algorithm　as　an　advanced　learning　method　for　social　big　data　within　next-generation　IIoT,　which　help　users　identify　the　information　collected　by　sensors　and　perform　further　processing.　This　method　employs　the　convolution　layer　to　capture　the　local　features　of　the　context　and　uses　the　variational　technique　to　reconstruct　feature　space　to　make　it　conform　to　the　normal　distribution.　In　addition,　the　improved　word2vec　model　based　on　topical　word　embedding　(TWE)　is　utilized　to　add　topical　information　to　word　vectors　to　distinguish　polysemy.　This　paper　takes　the　social　big　data　as　an　example　to　illustrate　the　way　of　the　proposed　algorithm　applied　in　the　next-generation　IIoT　and　utilizes　Cnews　dataset　to　verify　the　performance　of　proposed　method　with　four　evaluating　metrics　(i.e.,　recall,　accuracy,　precision,　and　F1-score).　Experimental　results　indicate　that　the　proposed　method　outperforms　word2vec-avg　and　CNN-AE　in　K-nearest　neighbor　(KNN),　random　forest　(RF),　and　support　vector　machine　(SVM)　classifiers　and　distinguishes　polysemy　effectively.　©　2023,　The　Author(s),　under　exclusive　licence　to　Springer　Science+Business　Media,　LLC,　part　of　Springer　Nature.