Cross-modal　3D　shape　retrieval　is　a　crucial　and　widely　applied　task　in　the　field　of　3D　vision.　Its　goal　is　to　construct　retrieval　representations　capable　of　measuring　the　similarity　between　instances　of　different　3D　modalities.　However,　existing　methods　face　challenges　due　to　the　performance　bottlenecks　of　single-modal　representation　extractors　and　the　modality　gap　across　3D　modalities.　To　tackle　these　issues,　we　propose　a　Heterogeneous　Dynamic　Graph　Representation　(HDGR)　network,　which　incorporates　context-dependent　dynamic　relations　within　a　heterogeneous　framework.　By　capturing　correlations　among　diverse　3D　objects,　HDGR　overcomes　the　limitations　of　ambiguous　representations　obtained　solely　from　instances.　Within　the　context　of　varying　mini-batches,　dynamic　graphs　are　constructed　to　capture　proximal　intra-modal　relations,　and　dynamic　bipartite　graphs　represent　implicit　cross-modal　relations,　effectively　addressing　the　two　challenges　above.　Subsequently,　message　passing　and　aggregation　are　performed　using　Dynamic　Graph　Convolution　(DGConv)　and　Dynamic　Bipartite　Graph　Convolution　(DBConv),　enhancing　features　through　heterogeneous　dynamic　relation　learning.　Finally,　intra-modal,　cross-modal,　and　self-transformed　features　are　redistributed　and　integrated　into　a　heterogeneous　dynamic　representation　for　cross-modal　3D　shape　retrieval.　HDGR　establishes　a　stable,　context-enhanced,　structure-aware　3D　shape　representation　by　capturing　heterogeneous　inter-object　relationships　and　adapting　to　varying　contextual　dynamics.　Extensive　experiments　conducted　on　the　ModelNet10,　ModelNet40,　and　real-world　ABO　datasets　demonstrate　the　state-of-the-art　performance　of　HDGR　in　cross-modal　and　intra-modal　retrieval　tasks.　Moreover,　under　the　supervision　of　robust　loss　functions,　HDGR　achieves　remarkable　cross-modal　retrieval　against　label　noise　on　the　3D　MNIST　dataset.　The　comprehensive　experimental　results　highlight　the　effectiveness　and　efficiency　of　HDGR　on　cross-modal　3D　shape　retrieval.