Association　categorization　approach　based　on　frequent　patterns　has　been　recently　presented,　which　builds　the　classification　rules　according　to　frequent　patterns　in　various　categories　and　classifies　the　new　text　employing　these　rules.　But　there　are　two　shortages　when　the　method　is　applied　to　classify　text　data:　one　is　that　the　method　ignores　the　information　about　word＇s　frequency　in　a　text;　another　is　that　the　rule　pruning　to　improve　the　classification　efficiency　will　lead　to　obvious　descending　of　accuracy　when　mass　rules　are　generated.　Therefore,　a　text　categorization　algorithm　based　on　frequent　patterns　with　term　frequency　is　presented.　This　study　illuminates　that　the　word　frequency　is　helpful　for　improving　the　accuracy　of　the　association　categorization　and　the　classification　rule　tree　can　improve　the　efficiency　of　the　association　classification.　The　result　of　experiments　shows　the　performance　of　association　classification　is　better　than　three　typical　text　classification　methods　Bayes,　kNN　(k　nearest　neighbor)　and　SVM　(support　vector　machines),　so　it　is　a　promising　text　classification　method.