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With the rapid development of information technology, how to enable teachers and students to quickly find and filter information of interest in massive collections has become a hot issue for many scholars. This paper enhances the traditional collaborative filtering algorithm by utilizing the knowledge-sharing model. Specifically, we calculate similarity using keyword information from items and dynamic information from users based on similarity calculations related to item features and user attributes. The relevant information about users and items is fully utilized to successfully alleviate the problem of new items and new users, and the entire process of collaborative filtering recommendations for libraries is designed. The improved collaborative filtering-based algorithm can achieve a recommendation accuracy of more than 60% and recommend more accurate books to users. The average recommendation rate of the book recommendation algorithm is 0.056 higher than the average recommendation rate of other recommender systems, indicating a higher recommendation rate that can better match the needs of users and alleviate the cold-start problem to a certain extent.

With the rapid development of network technology, a large amount of information fills the network world, and the performance of the current information extraction model to extract keyword information from a large number of data is insufficient. To solve the problem of insufficient extraction performance in traditional information extraction models, this paper combines text sorting algorithms with document topic generation models. A keyword information extraction model that combines the advantages of the two algorithms is proposed. The performance comparison experiment of this fusion algorithm shows that its accuracy and recall rates are 76.1% and 77.0%, respectively, which outperform the comparing algorithm. In the empirical analysis results of the information extraction model, it is found that the accuracy and precision rates of the proposed information extraction model are 80.16% and 77.54%, respectively, which are better than the comparing model. The proposed model of information extraction is of great importance for the development of the field of information extraction.

At present, most Chinese text summarization algorithms use the sequence-to-sequence model, but this model is prone to the problems of unknown words and incomplete content generation. To address these problems, we propose a new two-stage automatic text summarization method using keyword information and adversarial learning in this paper. On the one hand, the proposed method integrates the keyword information into the sequence-to-sequence model. The main information and keywords of the article are considered simultaneously through the attention mechanism to improve the information of summary generation. On the other hand, adversarial learning is introduced into the proposed model to avoid the problem that the semantic vector after passing through the encoder cannot save the context information better. Experiments are carried out on the Chinese dataset LCSTS, and the comparison results show that the proposed method has advantages in abstractive summarization. [ABSTRACT FROM AUTHOR]