Feature　extraction　is　essential　for　text　classification.　In　this　paper　we　discussed　the　basic　ideas　behind　word-clustering-based　feature　extraction.　Then　a　text　classification　method　for　feature　extraction　by　the　means　of　words　clustering　was　presented.　It　employed　an　improved　tree-structured　growing　self-organization　map　(TGSOM)　to　carry　out　word　clustering.　Also　a　new　formula　for　calculating　weights　was　developed　by　taking　account　of　the　distinction　between　clustered　word　features　and　plain　word　features.　Finally,　the　SPRINT　decision　tree　was　applied　to　complete　the　text　classification.　Experiments　showed　that　the　precision　of　text　classification　using　the　proposed　method　is　improved　by　4.32%.