Pathways and Practices for AI-Powered Digital Archives and Intelligent Services

Given the practical challenges of digital archives, such as resource heterogeneity, insufficient semantic utilization, and limited service capabilities, this research constructs an integrated AI-enabled digital archive system encompassing a "data layer—model layer—service layer—operation and maintenance layer." This system unifies the modeling and processing of archival metadata, full-text content, and user behavior, building a knowledge graph and semantic vector framework. It develops semantic retrieval and multi-dimensional navigation algorithms combining deep text encoding, KG embedding, and learning-based ranking, and then utilizes retrieval enhancement to generate intelligent question-answering and personalized recommendation models. Starting from the system level, it constructs a closed loop of intelligent service and manual review through service orchestration and human-machine collaboration. Offline evaluations and simulated online trials conducted on pilot platforms demonstrate that the proposed solution outperforms traditional methods in terms of retrieval accuracy, question-answering quality, recommendation click-through rate, and processing efficiency, while ensuring that tail latency and the proportion of manual intervention remain within a controllable range. This system provides a scalable technical approach and practical reference example for the intelligent upgrading of digital archives and information services.