As　an　important　step　in　natural　language　processing　(NLP),　text　classification　system　has　been　widely　used　in　many　fields,　like　spam　filtering,　news　classification,　and　web　page　detection.　Vector　space　model　(VSM)　is　generally　used　to　extract　feature　vectors　for　representing　texts　which　is　very　important　for　text　classification.　In　this　paper,　a　feature　selection　algorithm　based　on　synonym　merging　named　SM-CHI　is　proposed.　Besides,　the　improved　CHI　formula　and　synonym　merging　are　used　to　select　feature　words　so　that　the　accuracy　of　classification　can　be　improved　and　the　feature　dimension　can　be　reduced.　In　addition,　for　feature　words　selected　by　SM-CHI,　this　paper　presented　three　weight　calculation　algorithms　to　explore　the　best　feature　weight　update　method.　Finally,　we　designed　three　comparative　experiments　and　proved　the　classification　accuracy　is　the　highest　when　choosing　the　improved　CHI　formula　2,　set　the　threshold　a　to　0.8　and　use　the　largest　weight　among　the　synonyms　to　update　the　feature　weight,　respectively.