As　the　hub　of　urban　railway　transit,　metro　stations　portray　the　skeleton　structure　of　the　public　transit　network.　This　study　proposes　a　method　of　station　classification　from　the　dual　perspectives　of　network　structure　and　passenger　flow.　Each　perspective　considers　the　two　aspects,　one　is　the　characteristics　of　the　node　itself,　such　as　degree　and　the　entrance　and　exit　ridership;　another　considers　the　characteristics　of　the　influence　of　other　nodes,　such　as　betweenness　centrality　and　passing　flow.　Among　them,　the　importance　index　of　passing　flow　is　calculated　by　the　PageRank　algorithm.　According　to　these　characteristics,　metro　stations　are　classified　by　k-means　clustering　algorithm　after　dimensionality　reduction.　The　case　study　is　conducted　through　nearly　five　million　records　from　278　stations　in　Beijing.　From　the　classification　results,　stations　are　divided　into　six　categories.　Qualitative　and　quantitative　regulations　are　proposed　to　reduce　the　risk　of　high　ridership　stations　and　improve　the　operation　efficiency　for　few　ridership　stations.(C)　2022　Elsevier　B.V.　All　rights　reserved.