Topological　structures　of　real-world　graphs　often　exhibit　heterogeneity　involving　diverse　nodes　and　relation　types.　In　recent　years,　heterogeneous　graph　learning　methods　utilizing　meta-paths　to　capture　composite　relations　and　guide　neighbor　selection　have　garnered　considerable　attention.　However,　meta-path　based　approaches　may　establish　connections　between　nodes　of　different　categories　while　overlooking　relations　between　nodes　of　the　same　category,　decreasing　the　quality　of　node　embeddings.　In　light　of　this,　this　paper　proposes　a　Heterogeneous　Graph　Neural　Network　with　Adaptive　Relation　Reconstruction　(HGNN-AR2)　that　adaptively　adjusts　the　relations　to　alleviate　connection　deficiencies　and　heteromorphic　issues.　HGNNAR2　is　grounded　on　distinct　connections　derived　from　multiple　meta-paths.　By　examining　the　homomorphic　correlations　of　latent　features　from　each　meta-path,　we　reshape　the　cross-node　connections　to　explore　the　pertinent　latent　relations.　Through　the　relation　reconstruction,　we　unveil　unique　connections　reflected　by　each　meta-path　and　incorporate　them　into　graph　convolutional　networks　for　more　comprehensive　representations.　The　proposed　model　is　evaluated　on　various　benchmark　heterogeneous　graph　datasets,　demonstrating　superior　performance　compared　to　state-of-the-art　competitors.