Optimization of Library Book Classification and Retrieval System Based on Deep Learning

With the rapid growth of digital technology for book resources, the number of books in libraries has grown rapidly. Traditional book classification and retrieval methods are no longer able to efficiently meet the query needs of massive book data. The automatic classification of books is not only a fundamental task in book management and book recommendation algorithms, but also a key link in improving the efficiency of library services. Faced with this challenge, this article proposes a library book classification and retrieval system based on deep learning (DL). This system uses DL algorithm to deeply analyze and process book text data, automatically extract text features of books, construct efficient classification models, and achieve accurate classification of books. At the same time, the system also combines natural language processing (NLP) technology and user behavior analysis to provide personalized book recommendation services for users, accurately understanding their query intentions, and quickly retrieving book resources that meet their needs, greatly improving the accuracy and efficiency of retrieval. The experimental results show that compared with traditional book classification and retrieval methods, the system exhibits significant advantages in accuracy, efficiency, and user experience.