Distributed　data　streams　mining　is　increasingly　demanded　in　most　extensive　application　domains,　like　web　traffic　analysis　and　financial　transactions.　In　distributed　environments,　it　is　impractical　to　transmit　all　data　to　one　node　for　global　model.　It　is　reasonable　to　extract　the　essential　parts　of　local　models　of　subsidiary　nodes,　thereby　integrating　into　the　global　model.　In　this　paper　we　proposed　an　approach　SVDDS　to　do　this　model　integration　in　distributed　environments.　It　is　based　on　SVM　theory,　and　trades　off　between　the　risk　of　the　global　model　and　the　total　transmission　load.　Our　analysis　and　experiments　show　that　SVDDS　obviously　lowers　the　total　transmission　load　while　the　global　accuracy　drops　comparatively　little.