Traditional geometry engines can precisely construct boundary representation (BRep) models from procedural design histories, yet their internal representations are often opaque to data-driven systems and difficult to integrate into data management workflows. This opacity limits large-scale analysis, semantic retrieval, and interoperability across heterogeneous CAD environments. To address this challenge, this work introduces BRep-H, a data-centric framework that reformulates the transformation from construction histories to BRep structures as a structured data translation task. The framework decomposes topology and geometry reconstruction into modular language-model components and employs a vector-quantized autoencoder to discretize complex B-spline geometries into symbolic tokens. This formulation establishes a unified schema connecting symbolic modeling with analytic geometry, enabling interpretable, reconstructable, and queryable BRep data. Experiments on the BRep-History dataset demonstrate high structural validity and parametric fidelity, highlighting the potential of language-based schema translation for integrating geometric reasoning into data-driven CAD systems and for bridging AI modeling with database-level design knowledge management.