Recently,　heterogeneous　graphs　have　attracted　widespread　attention　as　a　powerful　and　practical　superclass　of　traditional　homogeneous　graphs,　which　reflect　the　multi-type　node　entities　and　edge　relations　in　the　real　world.　Most　existing　methods　adopt　meta-path　construction　as　the　mainstream　to　learn　long-range　heterogeneous　semantic　messages　between　nodes.　However,　such　schema　constructs　the　node-wise　correlation　by　connecting　nodes　via　pre-computed　fixed　paths,　which　neglects　the　diversities　of　meta-paths　on　the　path　type　and　path　range.　In　this　paper,　we　propose　a　meta-path-based　semantic　embedding　schema,　which　is　called　Heterogeneous　Graph　Embedding　with　Dual　Edge　Differentiation　(HGE-DED)　to　adequately　construct　flexible　meta-path　combinations　thus　learning　the　rich　and　discriminative　semantic　of　target　nodes.　Concretely,　HGEDED　devises　a　Multi-Type　and　multi-Range　Meta-Path　Construction　(MTR-MP　Construction),　which　covers　the　comprehensive　exploration　of　meta-path　combinations　from　path　type　and　path　range,　expressing　the　diversity　of　edges　at　more　fine-grained　scales.　Moreover,　HGE-DED　designs　the　semantics　and　meta-path　joint　guidance,　constructing　a　hierarchical　short-　and　long-range　relation　adjustment,　which　constrains　the　path　learning　as　well　as　minimizes　the　impact　of　edge　heterophily　on　heterogeneous　graphs.　Experimental　results　on　four　benchmark　datasets　demonstrate　the　effectiveness　of　HGE-DED　compared　with　state-of-the-art　methods.