To　solve　the　drawbacks　of　the　random　searching　based　algorithms　for　learning　Bayesian　networks,　we　introduced　the　Tabu　search　into　Bayesian　network　structure　learning　problems,　proposed　a　Tabu-search-based　Bayesian　network　structure　learning　algorithm　(TBN).　First,　the　new　algorithm　generates　the　neighborhood　solutions　by　add,　subtract　and　reverse　arc　operators.　And　then,　the　Tabu　list　and　aspiration　criteria　guide　the　search　procedure　corporately.　After　the　iteration　of　the　two　steps　above,　the　algorithm　will　finally　obtain　optimal　or　near　optimal　solutions.　The　experiment　results　on　the　benchmark　data　sets　show　that　TBN　has　a　simpler　structure　and　faster　speed.