Proton　exchange　membrane　fuel　cell　(PEMFC),　as　a　promising　power　source,　provides　a　feasible　solution　for　clean　and　low-carbon　energy　systems.　The　durability　problem　restricts　PEMFC　application　in　some　scenarios,　which　can　be　improved　by　the　prognostic　technology　indirectly.　This　paper　aims　to　develop　a　data-based　method　to　implement　the　short-term　and　long-term　prognostic　simultaneously,　and　the　developed　long-term　prognostic　can　be　performed　without　future　operation　information.　First,　the　short-term　prognostics　of　five　multi-step　ahead　forecasting　strategies　are　proposed　and　compared　based　on　a　long　short-term　memory　(LSTM)　network.　Results　show　that　the　multi-step　input　and　multi-step　output　(MIMO)　with　LSTM　strategy　has　a　better　performance　in　the　short-term　prognostics　under　the　test　conditions　of　the　stationary　and　dynamic　current.　Then,　the　hyper　parameters　of　the　prediction　model　are　determined　by　an　evolutionary　algorithm.　Furthermore,　in　the　longterm　prognostics　regime,　the　variable-step　long-term　method　is　proposed　and　rectified　by　the　short-term　prognostics.　Finally,　the　developed　remaining　useful　life　(RUL)　prediction　is　compared　with　a　model-based　extended　Kalman　filter.　The　average　root　mean　square　error　results　for　the　short-term　prognostics　of　two　conditions　are　0.00532　and　0.00538,　respectively.　The　RUL　estimations　of　two　PEMFCs　named　FC1　and　FC2　are　given　with　95%　and　90%　confidence　intervals,　respectively.　Consequently,　the　proposed　method　can　achieve　acceptable　accuracies　in　the　short-term　prognostic,　the　long-term　prognostic,　and　the　RUL　prediction.