Recent　years　have　witnessed　remarkable　achievements　in　video-based　action　recognition.　Apart　from　traditional　frame-based　cameras,　event　cameras　are　bio-inspired　vision　sensors　that　only　record　pixel-wise　brightness　changes　rather　than　the　brightness　value.　However,　little　effort　has　been　made　in　event-based　action　recognition,　and　large-scale　public　datasets　are　also　nearly　unavailable.　In　this　paper,we　present　an　event-based　action　recognition　framework　called　EV-ACT.　The　Learnable　Multi-Fused　Representation　(LMFR)　is　first　proposed　to　integrate　multiple　event　information　in　a　learnable　manner.　The　LMFR　with　dual　temporal　granularity　is　fed　into　the　event-based　slow-fast　network　for　the　fusion　of　appearance　and　motion　features.　A　spatial-temporal　attention　mechanism　is　introduced　to　further　enhance　the　learning　capability　of　action　recognition.　To　prompt　research　in　this　direction,　we　have　collected　the　largest　event-based　action　recognition　benchmark　named　$\mathbf{THU}^{\mathbf{E-ACT}}\mathbf{-50}$　and　the　accompanying　$\mathbf{THU}^{\mathbf{E-ACT}}\mathbf{-50-CHL}$　dataset　under　challenging　environments,　including　a　total　of　over　12,830　recordings　from　50　action　categories,　which　is　over　4　times　the　size　of　the　previous　largest　dataset.　Experimental　results　show　that　our　proposed　framework　could　achieve　improvements　of　over　14.5%,　7.6%,　11.2%,　and　7.4%　compared　to　previous　works　on　four　benchmarks.　We　have　also　deployed　our　proposed　EV-ACT　framework　on　a　mobile　platform　to　validate　its　practicality　and　efficiency.　IEEE