Research on Resource Integration and Intelligent Recommendation Mechanism of Digital Library under Big Data Environment

In today’s big data environment, the demand for digital transformation of traditional libraries is becoming more and more urgent. The article adopts BERT-BiLSTM-CRF model to extract digital library resources and retrograde entities, and constructs digital library resources knowledge graph. On the basis of digital library resources integration, it combines the collaborative filtering algorithm based on users and items to construct and improve the intelligent recommendation mechanism of digital book resources. The integration results of digital library resources and intelligent recommendation results are analyzed separately, and a survey on reader satisfaction is conducted. The recognition accuracy of this paper’s method is significantly higher than that of the traditional text-like processing data model. The collaborative filtering algorithm in this paper provides statistical analysis of the types of book resources read by each reader, and recommends the top 5 book types in terms of similarity to him/her. This paper’s method has better results in book resource division and book resource recommendation accuracy compared to other recommendation methods. The average value of readers’ satisfaction with the resource recommendation mechanism of the digital library in S city for each dimension and each index is more than 4 points.