Time　series　segmentation　is　a　crucial　area　of　research　in　time　series　analysis　as　it　can　reveal　meaningful　patterns　or　segments　hidden　within　time　series　data.　In　this　paper,　we　present　an　accurate　and　efficient　time　series　segmentation　method　that　combines　the　visibility　graph　method,　particle　swarm　optimization,　and　community　detection　algorithm.　We　start　by　applying　visibility　graph　theory　to　process　time　series　data,　resulting　in　a　corresponding　complex　network.　Next,　we　introduce　an　adaptive　particle　swarm　optimization　algorithm　with　modularity　Q　as　the　objective　function　to　optimize　community　detection.　Finally,　mapping　the　communities　back　to　the　nodes　of　the　time　series　yields　the　segmented　sequence.　Our　proposed　method　offers　high　segmentation　accuracy　and　low　time　complexity　(O(n(2))).　Experimental　results　demonstrate　that　our　approach　outperforms　existing　methods　in　terms　of　segmentation　accuracy　on　two　different　synthetic　datasets.　Furthermore,　when　applied　to　the　S&P500　index　dataset,　it　accurately　identifies　financial　cycles　and　key　financial　events.