Purpose:　Heterogeneous　graphs,　composed　of　diverse　nodes　and　edges,　are　prevalent　in　real-world　applications　and　effectively　model　complex　web-based　relational　networks,　such　as　social　media,　e-commerce　and　knowledge　graphs.　As　a　crucial　data　source　in　heterogeneous　networks,　Node　attribute　information　plays　a　vital　role　in　Web　data　mining.　Analyzing　and　leveraging　node　attributes　is　essential　in　heterogeneous　network　representation　learning.　In　this　context,　this　paper　aims　to　propose　a　novel　attribute-aware　heterogeneous　information　network　representation　learning　algorithm,　AAHIN,　which　incorporates　two　key　strategies:　an　attribute　information　coverage-aware　random　walk　strategy　and　a　node-influence-based　attribute　aggregation　strategy.　Design/methodology/approach:　First,　the　transition　probability　of　the　next　node　is　determined　by　comparing　the　attribute　similarity　between　historical　nodes　and　prewalk　nodes　in　a　random　walk,　and　nodes　with　dissimilar　attributes　are　selected　to　increase　the　information　coverage　of　different　attributes.　Then,　the　representation　is　enhanced　by　aggregating　the　attribute　information　of　different　types　of　high-order　neighbors.　Additionally,　the　neighbor　attribute　information　is　aggregated　by　emphasizing　the　varying　influence　of　each　neighbor　node.　Findings:　This　paper　conducted　comprehensive　experiments　on　three　real　heterogeneous　attribute　networks,　highlighting　the　superior　performance　of　the　AAHIN　model　over　other　baseline　methods.　Originality/value:　This　paper　proposes　an　attribute-aware　random　walk　strategy　to　enhance　attribute　coverage　and　walk　randomness,　improving　the　quality　of　walk　sequences.　A　node-influence-based　attribute　aggregation　method　is　introduced,　aggregating　neighboring　node　attributes　while　preserving　the　information　from　different　types　of　high-order　neighbors.　©　2025,　Emerald　Publishing　Limited.