In　order　to　explore　the　activity　pattern　and　regularity　of　public　transport　passengers,　this　study　constructed　multi-day　passenger　travel　activity　sequences　using　three　weeks　smart　card　data　in　Beijing　in　October　2020.　The　frequent　activity　pattern　sequences　of　passengers　were　mined　through　the　PrefixSpan　algorithm,　and　the　similarity　measure　method　of　activity　patterns　was　defined　based　on　the　longest　common　subsequence.　The　day-to-day　activity　sequence　similarity　of　individual　and　activity　pattern　similarities　among　different　passengers　were　calculated　respectively,　and　passengers　were　classified　according　to　activity　pattern　similarities　among　passengers　by　using　the　hierarchical　clustering　algorithm.　The　results　show　that　the　similarity　between　workdays　and　weekends　is　significantly　lower　than　that　within　workdays　or　weekends.　In　workdays,　the　activity　sequence　similarity　between　Friday　and　the　other　days　is　low.　Meanwhile,　the　activity　sequence　similarity　of　the　same　days　in　different　weeks　is　high.　The　result　of　hierarchical　clustering　shows　that　there　are　four　typical　activity　patterns,　including　entertainment　and　shopping　orientation,　life　orientation,　work　orientation　and　personal　affair　orientation.　Moreover,　the　day-to-day　activity　sequence　similarity　of　passenger　with　work　orientation　pattern　is　higher　than　that　of　passenger　with　other　activity　patterns.　The　research　results　in　this　paper　are　helpful　to　scientifically　formulate　accurate　public　transport　operation　management　and　service　policies.　©　2023　South　China　University　of　Technology.　All　rights　reserved.