Research And Implementation Of A Book Management System Based On Automatic Classification Of Chinese Books

Book classification is an extremely cumbersome and important thing,which has a link between the previous and the next in the book management process.However,the traditional book classification mainly relies on the author of the book to fill in,and it often carries a strong personal subjective consciousness.The library management department needs to reconsider Books are proofread and classified.In this process,errors are inevitable.In the era of artificial intelligence and big data,when text classification and recommendation systems have begun to be widely used,the library information management system cannot automatically classify books.With the increase in the number of books,it only relies on library managers to proofread a large number of books.And classification is obviously unrealistic,and among the huge amount of books,the cost for students to find their favorite books is also increasing.In order to realize the automatic classification of books and meet the actual requirements of colleges and universities,this paper designs and develops a set of book information management system,in addition to basic reader information management,book information management,system user information management,loan and return management and other basic functions,It mainly proposes an ERBERT-HMATT neural network model for automatic book classification.Through experimental analysis and verification,the classification model proposed in this paper achieves better classification results than other models.In addition,this paper studies the collaborative filtering algorithm of graph convolutional neural network,and applies the algorithm to the book recommendation module of the system,and recommends books of interest to readers based on the retrieval records of students and historical borrowing conditions.The main work of this paper is as follows:(1)In-depth and meticulous research on the classification algorithm,and according to the characteristics of Chinese books,an ERBERT-HMATT book classification model based on knowledge enhancement and deep learning is proposed.(2)In order to verify the effect of the experiment,this article uses Python,Seleninum and Phantom JS unbounded browser to crawl a large amount of book data on Doubanshu.In order to ensure the standardization and legitimacy of the data,this article carefully collates the crawled book-related data After the verification,the data after verification was cleaned.Through a large number of ablation experiments,the corresponding attributes of the book were selected as the book characteristics.Finally,the data was reasonably divided into a training set and a test set,and the performance of the model was verified.(3)In view of the known problems in the current book information management system,an online book information management system was redesigned and developed,and the functions of automatic book classification and book recommendation were added to the system.