Many　real-world　networks　can　be　treated　as　heterogeneous　information　networks　(HINs)　that　consist　of　various　types　of　nodes,　like　different　proteins　and　molecules　in　biological　networks　and　different　authors　and　papers　in　citation　networks.　Multiple　network　data　mining　tasks　can　be　conducted　on　HINs　to　capture　the　complex　relationships　between　multi-type　nodes.　In　recent　years,　random　walk　based　HIN　embedding　has　drawn　increasing　attention.　Furthermore,　the　meta-path　or　meta-graph　guided　random　walk　is　one　of　the　most　widely　used　techniques　in　HIN　embedding　methods.　However,　existing　HIN　embedding　methods　still　face　several　difficulties.　Firstly,　the　meta-paths　or　meta-graphs　often　need　to　be　predefined,　which　relies　heavily　on　domain　knowledge　and　incomplete　information　coverage.　Secondly,　these　methods　treat　all　relations　without　distinction,　which　inevitably　limits　the　capability　of　HIN　embedding.　Thirdly,　they　do　not　focus　on　preserving　finer-grained　meta-graph　semantics.　In　this　paper,　a　HIN　embedding　algorithm　based　on　adaptive　meta-schema　considering　relation　distinction　and　semantic　preservation　(HINEAS)　is　proposed.　In　order　to　avoid　the　selection　of　meta-paths　or　meta-graphs,　an　adaptive　meta-schema　extraction　is　designed.　In　heterogeneous　node　sequence　generation,　a　biased　random　walk　strategy　based　on　the　adaptive　meta-schema　is　presented　to　embed　the　different　relationships’　influence.　Finally,　an　enhanced　embedding　strategy　based　on　semantic　preservation　of　the　adaptive　meta-schema　is　proposed　to　effectively　extract　topology　and　preserve　the　meta-graph’s　fine-grained　semantics.　Experiments　on　real-world　datasets　show　that　HINEAS　significantly　outperforms　state-of-the-art　methods.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2025.