In　this　paper,　we　propose　an　efficient　segmentation　approach　in　order　to　divide　a　multivariate　time　series　through　integrating　principal　component　analysis　(PCA),　visibility　graph　theory,　and　community　detection　algorithm.　Based　on　structural　characteristics,　we　can　automatically　divide　the　high-dimensional　time　series　into　several　stages.　First,　we　adopt　the　PCA　to　reduce　the　dimensions;　thus,　a　low　dimensional　time　series　can　be　obtained.　Hence,　we　can　overcome　the　curse　of　dimensionality　conduct,　which　is　incurred　by　multidimensional　time　sequences.　Later,　the　visibility　graph　theory　is　applied　to　handle　these　multivariate　time　series,　and　corresponding　networks　can　be　derived　accordingly.　Then,　we　propose　a　community　detection　algorithm　(the　obtained　communities　correspond　to　the　desired　segmentation),　while　modularity　Q　is　adopted　as　an　objective　function　to　find　the　optimal.　As　indicated,　the　segmentation　determined　by　our　method　is　of　high　accuracy.　Compared　with　the　state-of-art　models,　we　find　that　our　proposed　model　is　of　a　lower　time　complexity　(　O　(　n　3　)　),　while　the　performance　of　segmentation　is　much　better.　At　last,　we　not　only　applied　this　model　to　generated　data　with　known　multiple　phases　but　also　applied　it　to　a　real　dataset　of　oil　futures.　In　both　cases,　we　obtained　excellent　segmentation　results.