Sleep　stage　classification　is　a　necessary　fundamental　method　for　the　diagnosis　of　sleep　diseases,　which　has　attracted　extensive　attention　in　recent　years.　Traditional　methods　for　sleep　stage　classification,　such　as　manual　marking　methods　and　machine　learning　algorithms,　have　the　limitations　of　low　efficiency　and　defective　generalization.　Recently,　deep　neural　networks　have　shown　improved　results　by　the　capability　of　learning　complex　pattern　in　the　sleep　data.　However,　these　models　ignore　the　intra-temporal　sequential　information　and　the　correlation　among　all　channels　in　each　segment　of　the　sleep　data.　To　solve　these　problems,　a　hybrid　attention　temporal　sequential　network　model　is　proposed　in　this　paper,　choosing　recurrent　neural　network　to　replace　traditional　convolutional　neural　network,　and　extracting　temporal　features　of　polysomnography　from　the　perspective　of　time.　Furthermore,　intra-temporal　attention　mechanism　and　channel　attention　mechanism　are　adopted　to　achieve　the　fusion　of　the　intra-temporal　representation　and　the　fusion　of　channel-correlated　representation.　And　then,　based　on　recurrent　neural　network　and　inter-temporal　attention　mechanism,　this　model　further　realized　the　fusion　of　inter-temporal　contextual　representation.　Finally,　the　end-to-end　automatic　sleep　stage　classification　is　accomplished　according　to　the　above　hybrid　representation.　This　paper　evaluates　the　proposed　model　based　on　two　public　benchmark　sleep　datasets　downloaded　from　open-source　website,　which　include　a　number　of　polysomnography.　Experimental　results　show　that　the　proposed　model　could　achieve　better　performance　compared　with　ten　state-of-the-art　baselines.　The　overall　accuracy　of　sleep　stage　classification　could　reach　0.801,　0.801　and　0.717,　respectively.　Meanwhile,　the　macro　average　F1-scores　of　the　proposed　model　could　reach　0.752,　0.728　and　0.700.　All　experimental　results　could　demonstrate　the　effectiveness　of　the　proposed　model.睡眠分期是研究睡眠疾病的重要途径，近年来受到了广泛关注。传统手工标记方法与传统机器学习算法存在效率低下、泛化性不足的问题，虽然近期流行的深度学习网络模型依靠其学习复杂特征的能力改善了睡眠分期结果，但仍存在着忽略片段内时序信息与通道相关性的问题。本文提出了一种混合注意力时序网络，利用循环神经网络取代较为传统的卷积神经网络，从时间角度提取多导睡眠图的时序特征；然后采用片段内时序注意力与通道注意力机制，实现信号片段内时序特征融合和通道相关性特征融合；再基于循环神经网络与片段间时序注意力机制，进一步实现信号片段间时序上下文特征融合；最终根据上述混合特征完成端到端自动睡眠分期。本文采用开源数据网站上包含多个多导睡眠图的睡眠数据集进行对比实验，实验结果表明本文模型能够优于　10　种典型基线模型，睡眠分期准确率分别可达到　0.801、0.801、0.717，平均　F1　分数可达到　0.752、0.728、0.700，验证了本文模型的有效性。.