Research on a Multilingual Cross-Context Retrieval System for Smart Libraries Based on Recurrent Neural Networks

With the rapid development of global smart libraries, the demand for semantic retrieval in multilingual environments is increasing. However, existing retrieval systems still have obvious deficiencies in handling semantic ambiguity and cross-context matching, which makes it difficult to meet the requirements of complex query scenarios. To this end, this paper proposes a multilingual cross-context retrieval model based on Bidirectional Long- and Short-Term Memory Network (BiLSTM) and attention mechanism, which aims to improve multi-lingual semantic understanding and retrieval relevance in smart libraries. The model achieves semantic unification through multilingual embedding, combines BiLSTM to capture contextual semantic relationships, and utilizes the attention mechanism to dynamically focus on key semantic fragments in documents, thus improving retrieval accuracy and relevance. The experimental results show that the model significantly outperforms traditional methods and unidirectional LSTM in multilingual retrieval tasks in terms of key metrics such as NDCG@10 and Recall@10, especially in complex semantic query scenarios. Meanwhile, the comparative experiments also verify the adaptability of the model in factual and open queries, showing good application prospects. The study shows that the method not only significantly improves the retrieval performance of smart libraries, but also provides a new technical path for multilingual intelligent retrieval.