超图天然适用于描述具有高阶、非二元相互作用的复杂网络化系统。由于其高维性及常含冗余结构，如何在保留相互作用本质结构的前提下简化超图表示，成为一项关键挑战。本文提出一种原理清晰、高效且无参数的信息论方法，用于剪枝超图中嵌套和/或冗余的结构，从而在局部异质性存在的情况下实现高阶相互作用的极小化表征。该方法自然可扩展至加权超图，在其中高阶拓扑结构与超边权重协同作用，以识别系统的结构骨干。我们在受控的合成超图上验证了该方法，并将其应用于来自多个领域的实证数据集，结果表明该方法可在不损失核心结构性信息的前提下实现显著稀疏化。

Hypergraphs provide a natural framework for describing complex networked systems with higher-order, non-dyadic interactions. Due to their high dimensionality and often redundant structure, a key challenge is to develop methods that simplify hypergraph representations while preserving the essential structure of interactions. Here we present a principled, efficient, and non-parametric information-theoretic method for pruning nested and/or redundant structures in hypergraphs, enabling a minimal representation of higher-order interactions in the presence of local heterogeneity. Our approach naturally extends to weighted hypergraphs, where higher-order topology and hyperedge weights combine to identify the system's structural backbone. We validate the method on controlled synthetic hypergraphs and apply it to empirical datasets from diverse domains, demonstrating substantial sparsification without loss of core structural information.