This　paper　proposes　an　approach　called　feature　weighted　confidence　with　support　vector　machine　(FWC-SVM)　to　incorporate　prior　knowledge　into　SVM　with　sample　confidence.　First,　we　use　prior　features　to　express　prior　knowledge.　Second,　FWC-SVM　is　biased　to　assign　larger　weights　for　prior　weights　in　the　slope　vector　than　weights　corresponding　to　non-prior　features.　Third,　FWC-SVM　employs　an　adaptive　paradigm　to　update　sample　confidence　and　feature　weights　iteratively.　We　conduct　extensive　experiments　to　compare　FWC-SVM　with　the　state-of-the-art　methods　including　standard　SVM,　WSVM,　and　WMSVM　on　an　English　dataset　as　Reuters-21578　text　collection　and　a　Chinese　dataset　as　TanCorpV1.0　text　collection.　Experimental　results　demonstrate　that　in　case　of　non-noisy　data,　FWC-SVM　outperforms　other　methods　when　the　retaining　level　is　not　larger　than　0.8.　In　case　of　noisy　data,　FWC-SVM　can　produce　better　performance　than　WSVM　on　Reuters-21578　dataset　when　the　retaining　level　is　larger　than　0.4　and　on　TanCorpV1.0　dataset　when　the　retaining　level　is　larger　than　0.5.　We　also　discuss　the　strength　and　weakness　of　the　proposed　FWC-SVM　approach.