Concerning the problem of high time complexity of traditional recommendation algorithms, a new recommendation model based on Latent Dirichlet Allocation (LDA) model was proposed. It was a data mining model applied to Book Recommendation (BR) in library management systems, named Book Recommendation_Latent Dirichlet Allocation (BR_LDA) model. Through the content similarity analysis of historical borrowing data of the target borrowers with other books, other books which had high content similarities with historical borrowing books of the target borrowers were gotten. Through the similarity analyses performed on the target borrowers' historical borrowing data and historical data from other borrowers, historical borrowing data of the nearest neighbors were gotten. Books which the target borrowers were interested in could be finally gotten by calculating the probabilities of the recommended books. In particular, when the number of recommended books is 4000, the precision of BR_LDA model is 6.2% higher than multi-feature method and 4.5% higher than association rule method; when the recommended list has 500 items, the precision of BR_LDA model is 2.1% higher than collaborative filtering based on the nearest neighbors and 0.5% higher than collaborative filtering based on matrix decomposition. The experimental results show that this model can efficiently mine data of books, reasonably recommend new books which belong to historical interested categories and new books in potential interested categories to the target borrowers.