Mining and searching association relation of scientific papers based on deep learning

Jie Song, MeiYu Liang, Zhe Xue, Feifei Kou, Ang Li

Abstract: There is a complex correlation among the data of scientific papers. The phenomenon reveals the data characteristics, laws, and correlations contained in the data of scientific and technological papers in specific fields, which can realize the analysis of scientific and technological big data and help to design applications to serve scientific researchers. Therefore, the research on mining and searching the association relationship of scientific papers based on deep learning has far-reaching practical significance.