Real-world heterogeneous data is commonly modeled as heterogeneous information networks (HINs). Building upon advancements in graph neural networks (GNNs), existing research has significantly progressed in semi-supervised and self-supervised paradigms for heterogeneous GNNs (HGNNs). However, these methods overlook inherent structural deficiencies in raw heterogeneous graphs. We identifies unique structural noise in HINs: missing potential critical edges and multi-relational semantically redundant edges, which force existing HGNNs to learn suboptimal representations on fixed topologies. Crucially, prior limited studies address only partial noise while remaining architecturally entrenched and tightly coupled with specific models. To break this bottleneck, we propose a plug-and-play Heterogeneous graph Structure ADaPter (HSADP) that simultaneously resolves task/model decoupling challenges while accounting for HIN-specific structural properties with with two core components: a dynamic homogeneous subgraph enhancer recovering latent topology across semantic views and a learnable heterogeneous edge discriminator dynamically suppressing redundant edges while collaboratively optimizing semantic graphs. Extensive experiments across multi-domain datasets demonstrate our method’s effectiveness and compatibility. The adapter significantly boosts node classification accuracy for multiple SOTA approaches and surpasses specially designed heterogeneous graph structure learning models.