The　recent　advances　in　pervasive　sensing　technologies　have　enabled　us　to　monitor　and　analyze　the　multi-channel　electroencephalogram　(EEG)　signals　of　epilepsy　patients　to　prevent　serious　outcomes　caused　by　epileptic　seizures.　To　avoid　manual　visual　inspection　from　long-term　EEG　readings,　automatic　EEG　seizure　detection　has　garnered　increasing　attention　among　researchers.　In　this　paper,　we　present　a　unified　multi-view　deep　learning　framework　to　capture　brain　abnormalities　associated　with　seizures　based　on　multi-channel　scalp　EEG　signals.　The　proposed　approach　is　an　end-to-end　model　that　is　able　to　jointly　learn　multi-view　features　from　both　unsupervised　multi-channel　EEG　reconstruction　and　supervised　seizure　detection　via　spectrogram　representation.　We　construct　a　new　autoencoder-basedmulti-view　learning　model　by　incorporating　both　inter　and　intra　correlations　of　EEG　channels　to　unleash　the　power　of　multi-channel　information.　By　adding　a　channel-wise　competition　mechanism　in　the　training　phase,　we　propose　a　channel-aware　seizure　detection　module　to　guide　our　multi-view　structure　to　focus　on　important　and　relevant　EEG　channels.　To　validate　the　effectiveness　of　the　proposed　framework,　extensive　experiments　against　nine　baselines,　including　both　traditional　handcrafted　feature　extraction　and　conventional　deep　learning　methods,　are　carried　out　on　a　benchmark　scalp　EEG　dataset.　Experimental　results　show　that　the　proposed　model　is　able　to　achieve　higher　average　accuracy　and　f1-score　at　94.37%　and　85.34%,　respectively,　using　5-fold　subject-independent　cross　validation,　demonstrating　a　powerful　and　effective　method　in　the　task　of　EEG　seizure　detection.