Time-series　clustering　of　view　counts　with　changes　in　online　time　can　identify　animated　series　with　similar　evolutionary　count　patterns　over　time,　which　may　help　companies　reduce　their　investment　risk.　For　time-series　data　of　animation　view　counts,　most　existing　time-series　clustering　methods　ignore　both　the　local　relationships　within　data　and　global　relationships　between　data,　making　it　challenging　to　identify　animated　series　with　similar　evolutionary　patterns.　Therefore,　we　propose　a　two-stage　deep　graph　clustering　method　that　involves　graph　data　construction　and　deep　graph　clustering.　Specifically,　graph　data　construction　converts　time-series　data　into　graph　data,　while　deep　graph　clustering　uses　a　temporal　convolutional　network　and　graph　convolutional　network　to　learn　features　from　the　time-series　data　and　graph　data,　respectively.　The　entire　model　is　then　trained　end-to-end　to　complete　the　clustering　task,　utilizing　a　dual　self-supervised　module.　This　study　utilizes　two　datasets　to　evaluate　the　clustering　performance　of　our　proposed　model　in　comparison　to　baseline　methods.　The　datasets　consist　of　view　count　data　from　185　animated　series　on　Tencent　Video　from　2018　to　2022,　as　well　as　a　public　time-series　BME　dataset.　The　experimental　results　demonstrate　that　our　model　outperforms　the　baseline　methods　on　both　datasets.　Additionally,　through　visualizing　the　identified　three　classes　of　evolutionary　patterns,　we　propose　new　product　development　strategies　for　animation　enterprises.　©　2023　Elsevier　Inc.