As　it　is　well　known　that　the　motivation　of　ensemble　learning　is　to　boost　a　strong　classifier　with　high　generalization　ability　from　a　weak　classifier.　However,　the　achievement　of　generalization　ability　is　often　at　great　cost　of　complexity　and　intense　computation.　In　this　paper　an　ensemble　learning　and　category　indicator　based　categorizing　method　is　proposed　and　Adaboost.MH　based　mechanism　is　developed　to　adaptively　compute　the　category　indicating　function　at　every　step.　Then　all　individual　category　indicating　functions　are　combined　with　weight　and　an　approximation　to　the　expected　category　indicating　function　is　obtained.　Based　on　the　combined　category　indicating　function,　a　classifier,　which　has　low　computational　cost,　flexibility　in　updating　with　new　features　and　suitable　for　real-time　applications　has　been　obtained.　Furthermore　it　is　proved　that　the　proposed　method　is　equivalence　to　ensemble　classifier　and　thereby　it　has　high　generalization　ability.　Experiments　on　the　corpus　of　TanCorp-12　show　that　the　proposed　method　can　achieve　good　performance　in　text　categorizing　tasks　and　outperform　many　text　categorizing　methods.