In　this　era　of　big　data,　data　are　diversified,　strongly　connected,　fragmented,　dynamic,　and　combined　with　dynamic　knowledge　fragments　to　optimize　the　distributed　storage　of　graphs　and　enable　fast　and　efficient　knowledge　graph　query　problems.　Presently,　the　distributed　storage　scheme　of　graph　data　has　a　large　number　of　hop　accesses　between　partitions,　which　leads　to　a　long　retrieval　response　time　and　is　not　conducive　to　fragment　knowledge　expansion.　According　to　the　characteristics　of　real-time　inflow　knowledge　fragments　and　the　storage　structure　and　principles　of　graph　databases,　the　Metis+　algorithm　is　proposed.　The　label　graph　is　used　as　the　initial　initialization　segmentation　graph,　and　it　is　roughened　to　reduce　the　cutting　of　the　large-weight　edge.　The　weighted　LND　algorithm　is　proposed　to　run　the　balancing　strategy　for　storage　and　assign　the　similar　nodes　and　closely　related　nodes　to　the　same　partition　to　the　greatest　extent,　which　minimizes　jump　accesses　between　the　partitions　during　retrieval.　©　2019,　Springer　Nature　Switzerland　AG.