To　accurately　express　text　information　by　vector　and　improve　the　performance　of　text　categorization,　a　term　weighting　scheme　with　enhanced　category　contribution　for　text　categorization　was　proposed.　Combining　the　term　weighting　factor　of　relevance　frequency　with　the　defined　category　contribution　function　based　on　posterior　probability,　the　scheme　gave　consideration　to　the　description　of　both　category　contribution　and　distributional　differences　among　categories　for　terms.　Experimental　results　on　the　four　standard　corpora　show　that　the　proposed　scheme　do　accurately　describe　the　contributions　of　different　features　on　the　classification,　optimize　the　works　of　text　representation　and　outperform　the　state-of-the-art　methods.