To　improve　the　performance　of　new　word　identification　in　Chinese　word　segment,　the　authors　propose　an　adaptive　method　for　Chinese　new　word　identification　based　on　multi-feature　method　for　offline　corpus　processing,　in　which　many　features,　including　context-entropy,　likelihood　ratios,　frequency　ratio　against　background　corpus　and　boundary-verification　with　basic　segmentation　are　introduced　to　evaluate　the　candidate　words.　And　all　of　the　features　are　integrated　into　an　adaptive　SVM　classifier.　Candidate　new　words　are　extracted　efficiently　on　PAT-Array　with　much　less　space　overhead　and　arbitrary　n-gram　words　can　be　identified　by　the　method.　The　results　show　that　the　method　can　run　fast　upon　new　word　identification　and　save　much　memory.