Temporal knowledge graph (TKG) reasoning aims to predict future events from historical facts. A key challenge for graph-based TKG models is to simultaneously capture higher-order structural dependencies and identify query-relevant temporal evidence, yet existing methods are largely limited to pairwise interactions or query-agnostic history aggregation. To address this, we propose SHAPE (Structural Hypergraph Augmentation with Personalized Event Memory), a plug-and-play enhancement framework for graph-based TKG models that strengthens the backbone at both structural and temporal levels without modifying its architecture. Specifically, Dual Hypergraph Pre-Enhancement(DHPE) constructs learnable entity and relation prototype hypergraphs with cross-view information exchange to inject higher-order structural priors before temporal encoding, while Query-Denoised Event Memory(QDEM) retrieves query-relevant recent events and converts them into subject enhancement and candidate-level scoring bias via structure-aware expert gating. Extensive experiments on three benchmarks with two heterogeneous graph backbones verify the effectiveness of SHAPE.