Existing　machine　learning　methods　for　classification　of　DNA　sequence　achieve　good　results,　but　these　methods　try　to　express　a　DNA　sequences　as　discrete　multi-dimensional　vector,　so　when　the　length　of　the　sequences　in　the　DNA　sequence　database　is　not　fixed　or　there　exists　some　omitted　characters,　these　methods　can　not　be　used　directly.　In　this　paper,　we　define　the　new　support　and　growth　rate　of　support　to　find　the　frequent　emerging　patterns　from　DNA　sequence　database,　and　present　a　classification　algorithm　FESP　based　on　the　frequent　emerging　sequence　patterns.　The　frequent　emerging　sequence　patterns　keep　the　information　provided　by　the　order　of　bases　in　gene　sequences　and　can　catch　interaction　among　bases.　FESP　algorithm　applies　classification　rules　that　are　constructed　by　frequent　emerging　sequence　patterns　of　each　class　to　classify　the　new　DNA　sequences.　This　method　can　work　on　sequences　with　different　lengths　or　omitted　character　and　shows　good　performance　©　2011　ACADEMY　PUBLISHER.