To　deal　with　the　problem　of　text　classification,　a　text　categorization　method　was　proposed　based　on　multiconlitron　from　the　perspective　of　piecewise　learning.　First,　text　sample　preprocessing　including　feature　selection　and　feature　weighting　was　performed.　Then,　the　multiconlitron　was　constructed　by　using　growing　support　multiconlitron　algorithm　(GSMA)　and　support　multiconlitron　algorithm　(SMA)　respectively　for　text　classification.　Inspired　by　the　idea　of　maximum　interval　of　support　vector　machine,　the　classification　of　two　kinds　of　data　by　integrating　the　linear　classifier　was　achieved　by　this　model,　which　had　the　advantages　of　small　computation　cost　and　strong　adaptive　ability.　Experiments　on　standard　text　data　sets　show　that　the　proposed　method　has　a　good　performance　on　text　classification　and　the　comparison　results　with　some　other　typical　text　classification　methods　also　verifies　the　effectiveness　of　the　proposed　method.　©　2017,　Editorial　Department　of　Journal　of　Beijing　University　of　Technology.　All　right　reserved.