Abstract: In the work of scientific and technological literature retrieval, it is very important to give a reasonable correlation ranking from a professional point of view. The traditional PageRank algorithm uses the method of evenly distributing similarity weights, but this method will cause the unreasonable results of literature ranking. Therefore, an algorithm combining deep learning method and PageRank is proposed to improve the reliability of literature relevance ranking. Firstly, the Siamese BERT ( Bidirectional Encoder Representation from Transformers) network with attention pooling is used to calculate the similarity between literature and citations, and then the similarity between literature and citations contained in literature is normalized. Finally, the normalized similarity is used as the distribution weight to calculate the ranking results of citation network. The experimental results show that compared with the traditional PageRank algorithm, the correlation of the retrieval results of this method is improved by more than 6% , which is more suitable for citation network analysis of scientific and technological literature.

Key words: PageRank')">

PageRank, literature similarity; bidirectional encoder representation from transformers (BERT), paper ranking