Discretization　of　continuous　attributes　is　one　of　the　most　important　issues　in　network　data　preprocessing.　In　this　paper,　a　discretization　algorithm　of　continuous　attributes　based　on　cloud　model　and　information　entropy　is　proposed.　Making　use　of　cloud　transform,　the　proposed　algorithm　partitions　the　domain　of　every　continuous　attribute　into　many　concepts　represented　by　cloud　models.　The　uncertain　boundary　of　cloud　model　is　more　appropriate　for　actual　data　distribution.　Define　information　entropy　for　every　candidate　cloud　that　treated　as　a　measuring　of　importance.　On　the　basis　of　that,　select　appropriate　neighboring　concepts　and　merge　them.　It　could　increase　the　information　granularity　of　information　system.　Then　utilize　the　fuzziness　of　cloud　model　for　realizing　the　adaptive　adjustment　of　boundary　data,　so　as　to　improve　consistency　level　in　decision　table.　By　employing　the　new　algorithm,　the　experiments　on　Iris　data　set　reached　the　expectation.　The　results　show　that　the　algorithm　is　feasible　and　efficient.　©　2009　IEEE.