[Purpose/Significance] In response to the practical demand for high-quality data support and trustworthy operation of domain large models in specialized and high-risk application scenarios, this study systematically examines the basic connotation, key characteristics, and systematic construction path of the domain large model data foundation. It aims to provide a theoretical framework and methodological support for the practical deployment of trustworthy artificial intelligence, and to offer systematic references for the standardized construction and governance practice of the domain large model data foundation. [Method/Process] Based on a systematic review of the relevant literature, this study clarified the conceptual connotation, major characteristics, and constituent elements of the domain large model data foundation. It introduced the theory of layered modular architecture to construct a layered framework for the domain large model data foundation, mainly covering the data resource layer, knowledge organization layer, knowledge storage and semantic indexing layer, model layer, algorithm layer, application layer, and governance and operational support layer, so as to characterize the overall mechanism of the coordinated operation of data, knowledge, and models. Finally, a desk research method based on public evidence and a cross-case comparison approach were employed to demonstrate the contextual applicability of the framework. [Result/Conclusion] Cross-domain comparisons show that the proposed framework can explain the common mechanisms underlying external knowledge memory access, evidence-constrained generation, and compliance-audit output chains in reference systems across different domains. On this basis, the study further distills reusable methodological elements such as data contracts, index configuration, and evaluation baselines, thereby validating the applicability and transferability of the framework across domains.