The hash-based digital signature (HBS) algorithm is becoming essential in the field of digital signatures due to its quantum resistance. However, current research on HBS algorithms primarily focuses on the design of individual algorithms, lacking a comprehensive database that encompasses complete knowledge of HBS algorithms. This poses significant challenges for cross-scheme performance comparisons, and algorithm recommendations based on varying scenario requirements. To fill this blank, we propose an HBS algorithmic database construction method based on Chain-of-Thought (CoT) guidance, which innovatively adopts CoT and snowballing technologies to effectively enhance the completeness of the database and the consistency of algorithm relationships. Specifically, we first designed a cryptography-specific prompt template called Chain-of-Thought Knowledge Extraction (CoT-KE) to guide large models in feature inference, thereby accurately extracting algorithm-related content. Then we developed a systematic approach that combines the Snowballing with Relationship Type Identification and Enhancement (SRTIE) to construct a cross-literature knowledge network, tracking the derivative paths of algorithms and improving the integrity of algorithm knowledge. Moreover, we proposed a verification framework called Multi-View Quality Validation and Consistency (MV-QVC) to enhance the accuracy of the information extracted from algorithms. Extensive experiments demonstrate that our method achieves an accuracy of 94.3% in feature extraction and enhances data integrity by 25–30% compared to general cross-domain database methods, while achieving 95.5% algorithm result consistency and 91.7% algorithm relationship identification consistency.