In　the　era　of　big　data,　the　internet　produces　vast　amounts　of　data　every　day,　among　which　text　data　occupies　the　main　position.　It　is　difficult　for　manual　processing　to　deal　with　the　increasing　growth　rate　of　text　data.　As　basis　of　most　natural　language　processing　(NLP)　tasks,　text　representation　aims　to　transform　text　into　a　vector　that　can　be　processed　by　computer　without　losing　the　original　important　semantic　information.　It　has　become　an　important　research　direction　in　the　field　of　NLP　that　effectively　organize,　manage　and　quickly　use　the　complex　text　information　to　extract　useful　semantics　from　it.　Therefore,　a　text　feature　representation　model　based　on　convolutional　neural　network　(CNN)　and　variational　auto　encoder　(VAE)　is　proposed　to　extract　the　text　features　and　apply　the　obtained　text　feature　representation　to　text　classification　scene.　CNN　is　used　to　extract　local　features　and　VAE　makes　the　extracted　features　more　consistent　with　Gaussian　distribution.　The　proposed　method　has　best　performance　compared　with　w2v-avg　and　CNN-AE　in　k-nearest　neighbor　(KNN),　random　forest　(RF)　and　support　vector　machine　(SVM)　classification　algorithms.　©　Springer　Nature　Switzerland　AG　2020.