Community　detection　is　essential　for　identifying　cohesive　groups　in　complex　networks.　Artificial　benchmarks　are　critical　for　evaluating　community　detection　algorithms,　offering　controlled　environments　with　known　community　structures.　However,　existing　benchmarks　mainly　focus　on　homogeneous　networks　and　overlook　the　unique　characteristics　of　heterogeneous　networks.　This　paper　proposes　a　novel　artificial　benchmark,　called　ABCD-HN　(Artificial　Network　Benchmark　for　Community　Detection　on　Heterogeneous　Networks),　for　community　detection　in　heterogeneous　networks.　This　benchmark　enables　the　generation　of　artificial　heterogeneous　networks　with　controllable　community　quantity,　node　quantity,　and　community　complexity.　Additionally,　an　evaluation　framework　for　artificial　heterogeneous　networks　is　proposed　to　assess　their　effectiveness.　Experimental　results　demonstrate　the　effectiveness　and　usability　of　ABCD-HN　as　a　benchmark　for　artificial　heterogeneous　networks.　©　2024,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.