Community　evolution　tracking　is　widely　used　in　complex　network　analysis,　which　analyzes　and　identifies　how　communities　evolve　over　time　based　on　dynamic　community　detection.　However,　the　current　incremental　dynamic　community　detection　method　has　the　phenomenon　of　’concept　drift’,　which　leads　to　inaccurate　tracking　of　community　evolution.　Although　some　studies　on　dynamic　community　detection　have　proposed　to　perform　global　community　detection　on　the　network　by　timing　or　when　the　error　information　accumulates　to　a　certain　extent,　this　method　will　lead　to　a　sudden　change　in　community　division,　which　will　interrupt　the　evolution　sequence　of　some　communities　and　bring　great　difficulties　to　the　continuous　tracking　of　community　evolution.　In　addition,　the　current　community　evolution　tracking　methods　match　the　communities　of　two　adjacent　snapshots,　and　it　is　difficult　to　capture　the　fine-grained　evolution　process　within　the　community.　This　paper　proposes　a　community　evolution　tracking　algorithm　based　on　high-order　neighbor　consideration　and　node　change　identification.　Firstly,　based　on　an　incremental　update　strategy　that　considers　high-order　neighbors,　we　adaptively　expand　the　scope　of　incremental　updates　to　reduce　the　accumulation　of　error　information.　Secondly,　through　the　node　change　identification　strategy　to　mine　fine-grained　community　evolution　events.　Experiments　show　that　our　method　can　effectively　track　the　evolution　of　communities　in　dynamic　networks.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2025.