Automatic　sleep　staging　plays　an　essential　role　in　the　diagnosis　of　sleep　disorders.　While　significant　advancements　have　been　achieved　in　the　field　of　automatic　sleep　staging　research,　three　challenges　persist:　(1)　The　problem　of　how　to　effectively　extract　and　utilize　the　features　of　multi-channel　physiological　signals　has　not　been　well　addressed.　(2)　The　temporal　correlation　of　sleep　signals　is　very　strong,　so　how　to　design　the　network　to　learn　the　correlation　between　local　temporal　features　becomes　significant.　(3)　Due　to　there　are　many　differences　in　physiological　signals　between　different　individuals,　this　difference　seriously　affects　the　generalization　of　model　training　methods.　To　address　the　aforementioned　challenges,　the　Convolutional　Transformer　with　Domain　Adversarial　Learning　for　Multi-Channel　Sleep　Stage　Classification　(CT-DAL)　is　proposed.　This　method　enables　automated　extraction　of　spatio-temporal　features　from　multi-channel　signals　and　utilizes　multi-head　attention　to　learn　the　correlations　between　these　diverse　features.　Finally,　by　integrating　Domain　Adversarial　Learning　into　the　network,　the　common　features　between　different　individuals　can　be　learned　to　enhance　model＇s　generalization　capability.　In　this　study,　the　efficacy　of　each　module　is　demonstrated　through　ablation　experiments,　and　the　experimental　results　from　baseline　comparisons　provide　compelling　evidence　that　the　proposed　model　outperforms　all　baseline　models.　©　2023　IEEE.