<p>This study addresses the problem of transforming unstructured engineering design requirements into machine-interpretable knowledge for automated verification. Conventional engineering verification approaches typically assume that design knowledge is already formalized, creating a critical bottleneck when requirements are described in natural language documents. To overcome these limitations, this study presents an automated approach for converting unstructured documents into Computer-Aided Design (CAD)-verifiable design rules. Design variables and requirements are first extracted and consolidated to resolve duplication arising from differences in expression. To enable reliable knowledge alignment under linguistic variability, document-level variables are semantically mapped to standardized parameters using a Sentence-BERT (SBERT)-based similarity model and a codebook. The extracted requirements are formalized into symbolic mathematical representations. The resulting rules are organized into a three-layer architecture inspired by a reinterpretation of the Requirement–Applicability–Selection–Exception (RASE) structure, enabling dynamic selection of applicable rules according to the relevant design features. The experimental results demonstrate that the proposed framework can reliably transform document-level requirements into a consistent and reusable design knowledge structure, achieving substantial coverage within the executable requirement scope and complete resolution of redundant variables. Moreover, the automated verification results on CAD models were fully consistent with manual verification outcomes.</p>