In　order　to　better　predict　the　short-term　passenger　flow　of　urban　rail　transit，a　prediction　method　based　on　the　recurrent　neural　network　model　is　proposed.　Firstly，based　on　the　actual　passenger　flow　data　of　each　rail　transit　station，the　Pearson　correlation　coefficient　is　used　to　determine　the　influencing　factors　of　short-term　passenger　flow　of　rail　transit，such　as　the　weather　conditions，historical　passenger　flow，whether　it　is　a　peak　time　period，whether　it　is　a　working　day，etc.　Secondly，the　K-means　clustering　algorithm　is　used　to　classify　rail　transit　stations　into　three　types：high，medium，and　low　passenger　flow　stations.　Then　the　distribution　of　passenger　flow　for　each　station　type　in　time　and　space　is　analyzed，to　determine　the　peak　period　of　passenger　flow　for　each　station　type.　Finally，two　urban　rail　transit　short-term　passenger　flow　prediction　methods　based　on　long-short　term　memory　neural　network（LSTM）and　gated　recurrent　unit（GRU）respectively　are　proposed　to　predict　the　passenger　flow　of　each　type　of　station　in　different　time　period.　The　experimental　results　show　that　5　min　is　the　best　time　granularity　for　short-term　passenger　flow　prediction　of　the　two　models.　In　this　time　granularity，the　overall　performance　of　the　GRU　model　is　better　than　the　LSTM　model.　©　2023　Editorial　Board　of　Jilin　University.　All　rights　reserved.