Graph　neural　networks　(GNNs)　have　beenwidely　used　for　graph　structure　learning　and　achieved　excellent　performance　in　tasks　such　as　node　classification　and　link　prediction.　Real-world　graph　networks　imply　complex　and　various　semantic　information　and　are　often　referred　to　as　heterogeneous　information　networks　(HINs).　Previous　GNNs　have　laboriously　modeled　heterogeneous　graph　networks　with　pairwise　relations,　in　which　the　semantic　information　representation　for　learning　is　incomplete　and　severely　hinders　node　embedded　learning.　Therefore,　the　conventional　graph　structure　cannot　satisfy　the　demand　for　information　discovery　in　HINs.　In　this　article,　we　propose　an　end-to-end　hypergraph　transformer　neural　network　(HGTN)　that　exploits　the　communication　abilities　between　different　types　of　nodes　and　hyperedges　to　learn　higher-order　relations　and　discover　semantic　information.　Specifically,　attention　mechanisms　weigh　the　importance　of　semantic　information　hidden　in　original　HINs　to　generate　useful　meta-paths.　Meanwhile,　our　method　develops　a　multi-scale　attention　module　to　aggregate　node　embeddings　in　higher-order　neighborhoods.　We　evaluate　the　proposed　model　with　node　classification　tasks　on　six　datasets:　DBLP,　ACM,　IBDM,　Reuters,　STUD-BJUT,　and　Citeseer.　Experiments　on　a　large　number　of　benchmarks　show　the　advantages　of　HGTN.