In　the　territory　of　text　categorization,　the　distribution　and　quality　of　sample　set　is　highly　influential　to　categorization　result.　Associated　rule　categorization　ARC-BC　is　effective　under　common　circumstances.　The　accuracy　of　categorization　obviously　falls　as　distribution　of　feature　words　of　training　samples　is　uneven.　In　this　paper,　a　Chinese　text　classification　approach　was　proposed　based　on　sample　weighting　associated　rules　(SW-ARC).　The　approach　improved　substantial　classification　efficiency　by　performing　selfadapting　sample　weights　adjustment.　Experiment　result　shows　SW-ARC　can　solve　the　quality　fall　caused　by　uneven　distribution　of　feature　words.　Macroaverage　recall　of　open　test　increases　from　50%　of　ARC-BC　to　70%　of　SW-ARC,　Macro-average　precision　increases　from　28%　of　ARC-BC　to　70%　of　SW-ARC.　©　Springer-Verlag　Berlin　Heidelberg　2005.