The current digital transformation of archives mainly faces three major bottlenecks: low efficiency of manual quality inspection, a low pass rate of digitized copies of paper archives, a lack of intelligent verification methods for multimodal archival data, and an incomplete quality traceability system. In this context, this project will deeply integrate the deep reasoning capability of DeepSeek with archival resource quality control. First, in response to the quality control pain points in the current digital transformation of archives, it systematically reviews the practical difficulties and technical needs of comprehensive archives at all levels in data quality control. Second, it develops intelligent quality inspection technology for multimodal archival data to solve the problem of heterogeneous data governance. Third, it develops integrated technology for archival classification and automatic metadata generation based on deep reasoning, reshaping the technical paradigm of archival quality inspection. Finally, it builds an adaptive optimization and quality evaluation system for large models, enabling large model technology across the full cycle from laboratory validation to industry deployment.