The　exploration　of　self-supervised　information　mining　of　heterogeneous　datasets　has　gained　significant　traction　in　recent　years.　Heterogeneous　graph　neural　networks　(HGNNs)　have　emerged　as　a　highly　promising　method　for　handling　heterogeneous　information　networks　(HINs)　due　to　their　superior　performance.　These　networks　leverage　aggregation　functions　to　convert　pairwise　relations-based　features　from　raw　heterogeneous　graphs　into　embedding　vectors.　However,　real-world　HINs　contain　valuable　higher-order　relations　that　are　often　overlooked　but　can　provide　complementary　information.　To　address　this　issue,　we　propose　a　novel　method　called　Self-supervised　Nodes-Hyperedges　Embedding　(SNHE),　which　leverages　hypergraph　structures　to　incorporate　higher-order　information　into　the　embedding　process　of　HINs.　Our　method　decomposes　the　raw　graph　structure　into　snapshots　based　on　various　meta-paths,　which　are　then　transformed　into　hypergraphs　to　aggregate　high-order　information　within　the　data　and　generate　embedding　representations.　Given　the　complexity　of　HINs,　we　develop　a　dual　self-supervised　structure　that　maximizes　mutual　information　in　the　enhanced　graph　data　space,　guides　the　overall　model　update,　and　reduces　redundancy　and　noise.　We　evaluate　our　proposed　method　on　various　real-world　datasets　for　node　classification　and　clustering　tasks,　and　compare　it　against　state-of-the-art　methods.　The　experimental　results　demonstrate　the　efficacy　of　our　method.　Our　code　is　available　at　https://github.com/limengran98/SNHE.