The　value　of　Internet　short　texts　is　increasingly　prominent,　and　traditional　classification　methods　cannot　be　applied　to　short　texts　with　weak　feature　expression.　In　this　regard,　this　paper　proposes　a　Chinese　Short　Text　Classification　method　based　on　Word　Embedding　and　LSTM　with　feature　enhancement　(hereinafter　called　CSTCFE-WE-LSTM).　This　method　uses　word　embedding　learned　from　Wikipedia　corpus　as　initial　features　for　the　model,　and　then　uses　category　factors　and　TF-IDF　to　generate　weights　to　enhance　features.　Finally,　it　uses　a　6-layer　neural　network　for　classification,　which　includes　a　word　embedding　layer,　two　LSTM　layers,　a　Dropout　layer,　and　two　fully　connected　layers.　In　order　to　verify　the　method　CSTCFE-WE-LSTM,　we　collected　short　text　sets　on　3　topics,　and　reached　following　conclusions:　1.　In　the　best　model,　P,　F　and　other　indicators　are　better　than　the　classifier　of　using　Wikipedia　word　embedding　and　KNN.　2.　For　texts　with　sentences　average　length　less　than　10　words,　the　effect　of　two-layer　LSTM　is　better　than　that　of　single-layer　LSTM,　and　the　effect　is　better　when　the　number　of　single-layer　nodes　is　50.　3.　The　effect　of　feature　enhancement　is　better　for　the　health　category　than　the　commercial　category.　©　2021　IEEE.