Corpus Indexing Database Construction based on Deep Learning Algorithm

This paper explores a novel method for constructing a corpus index database using deep learning algorithms. The author reviews the evolution of these algorithms and their central role in natural language processing, particularly in corpus construction and information retrieval. The paper details how deep learning algorithms, in conjunction with the hash model, efficiently organize large datasets for corpus indexing. In addition, a new deep information retrieval algorithm is presented that balances efficiency and improved search results. The author illustrates the practical application of bilingual parallel corpora in machine translation and validates its feasibility. These results not only contribute to the methodology of translation research, but also provide a scientific tool for translation practice. Test results show a remarkable performance improvement of the proposed model compared to traditional methods, achieving accuracy between 98.45% and 98.86%, as shown in Table II, confirming its robustness for broader information retrieval application.