File　carving　is　a　technique　of　recovering　data　from　disk　without　depending　on　the　File　System,　and　the　key　step　is　the　extraction　and　reassembly　of　file　fragments.　Efficient　recognition　and　extraction　of　file　fragments　is　not　only　the　prerequisite　of　recovering　file,　but　also　the　guarantee　of　a　low　false　positive　rate　and　high　accuracy　Digital　Forensics.　In　this　paper,　when　the　entropy　of　file　fragments　is　low,　the　validation　algorithms　I　used　for　the　extraction　are　header/footer　validation　and　entropy　feature　extraction　validation,　but　when　the　entropy　of　file　fragments　is　high,　besides　the　previous　two　algorithms　I　introduced　Bloom　filter　feature　extraction　validation,　byte　frequency　distribution　(BFD)　feature　extraction　validation　and　support　vector　machine　(SVM)　with　supervised　learning　ability　to　detect　the　type　of　file　fragments.　After　the　extraction,　I　used　Parallel　Unique　Path　(PUP)　for　the　reassembly　of　file　fragments.　I　used　DFRWS　2007　carving　challenge　data　set　to　test　my　method　and　the　result　is　better　than　only　using　entropy　to　classify　multiple-type　files　especially　in　the　case　of　high　entropy.