Abstract：Research on Full-text Recommendation System Based on Language Technology Applied In Library In recent yearsrecommendation systems have been widely used in the commercial fieldand many e-commerce platforms and merchants have adopted the model of recommendation systems as a way to sell goodsHoweverwhen the products features are insufficientor the users evaluation of the product is not muchit is difficult for the recommender system to recommend the product that meets the customers expectationsFor examplewhen recommending books in the libraryyou will find out that the books cannot be like clothes and can use rich and diverse ontology features such as brandmaterialcolorand length as the basis for the recommendation systemIn additionwe found that only a few users will Evaluate or comment on books during the users borrowing processTo sum upthis study will explore how to recommend books through natural language processing in the case of insufficient book features and insufficient user ratings and reviewsRetrieval and IndexingRecommended books to users through NLP analysis of books to usersLibrary AutomationThe automation recommendation system can help the library increase the borrowing rateThis study focuses on how to recommend books through natural language processingNLPwhen the characteristics of insufficient book features and insufficient reviews and commentsThe study mainly uses the summary of each book for correlation comparisonand uses the text in the abstract as the recommendation systemIt is similar to itand the full text is used to recommend it