Sleep　is　essential　for　human　life.　Automatic　sleep　stage　classification　based　on　polysomnography　(PSG)　has　attracted　extensive　attention　recently,　which　is　fundamental　for　the　diagnosis　of　sleep　diseases.　Single-channel　sleep　staging　methods　usually　ignore　the　bioinformation　of　some　modalities　to　reduce　the　cost　of　signal　acquisition,　while　multi-channel　methods　consider　more　modalities　with　higher　model　complexity.　To　this　end,　we　propose　an　effective　and　simple　network　(ESNet),　to　effectively　learn　important　features　from　multi-channel　PSG　and　reduce　model　parameters　for　automatic　sleep　staging.　This　model　utilizes　bidirectional　gated　recurrent　unit　(BiGRU)　to　capture　epoch-level　waveform　information　and　sequence-level　sleep　transitions　of　PSG,　and　adopting　a　simple　convolutional　block　for　epoch-level　channel-wise　feature　fusion.　The　sequence-level　learned　representation　is　fed　into　a　softmax　layer　to　train　an　end-to-end　model.　Experimental　results　on　three　public　datasets　show　that　ESNet　obtains　the　best　classification　performance　with　satisfied　model　parameters　compared　with　several　baselines.　And　the　convolutional　block　incorporates　necessary　channel-wise　information　for　sleep　staging.　Our　work　provides　an　effective　and　simple　architecture　to　model　PSG,　which　is　promising　for　deep　learning-based　convenient　sleep　monitoring　in　the　future.　©　2023　ACM.