Forecasting　the　short-term　metro　ridership　is　an　important　issue　for　operation　management　of　metro　systems.　However,　it　cannot　be　solved　well　by　the　single　long　short-term　memory　(LSTM)　neural　network　alone　for　the　irregular　fluctuation　caused　by　various　factors.　This　paper　proposes　a　hybrid　algorithm　(STL-LSTM)　which　combines　the　addition　mode　of　Seasonal-Trend　decomposition　based　on　Loess　(STL)　and　the　LSTM　neural　network　to　mitigate　the　influences　of　irregular　fluctuation　and　improve　the　performance　of　short-term　metro　ridership　prediction.　First,　the　original　series　is　decomposed　into　three　sub-series　by　the　addition　mode　of　STL.　Then,　the　LSTM　neural　network　is　employed　to　predict　each　decomposed　series.　Finally,　all　the　predicted　outputs　are　merged　as　the　overall　output.　The　results　show　that　the　STL-LSTM　model　can　achieve　higher　accuracy　than　the　single　LSTM　model,　support　vector　regression　(SVR),　and　the　EMD-LSTM　model　which　combines　the　empirical　mode　decomposition　and　the　LSTM　neural　network.　©　2013　IEEE.