The　daily　travel　patterns　(DTPs)　present　short-term　and　timely　characteristics　of　the　users＇　travel　behaviour,　and　they　are　helpful　for　subway　planners　to　better　understand　the　travel　choices　and　regularity　of　subway　users　(SUs)　in　details.　While　several　well-known　subway　travel　patterns　have　been　detected,　such　as　commuting　modes　and　shopping　modes,　specific　features　of　many　patterns　are　still　confused　or　omitted.　Now,　based　on　the　automatic　fare　collection　(AFC)　system,　a　data-mining　procedure　to　recognize　DTPs　of　all　SUs　has　become　possible　and　effective.　In　this　study,　DTPs　are　identified　by　the　station　sequences　(SSs),　which　are　modelled　from　smart　card　transaction　data　of　the　AFC　system.　The　data-mining　procedure　is　applied　to　a　large　weekly　sample　from　the　Beijing　Subway　to　understand　DTPs.　The　results　show　that　more　than　93%　SUs　of　the　Beijing　Subway　travel　in　7　DTPs,　which　are　remarkably　stable　in　share　and　distribution.　Different　DTPs　have　their　own　unique　characteristics　in　terms　of　time　distribution,　activity　duration　and　repeatability,　which　provide　a　wealth　of　information　to　calibrate　different　types　of　users　and　characterize　their　travel　patterns.