An Empirical Study on Knowledge Aggregation in Academic Virtual Community Based on Deep Learning

Abstract

Abstract Academic virtual community provides an environment for users to exchange knowledge, so it gathers a large amount of knowledge resources and presents a trend of rapid and disorderly growth. We learn how to organize the scattered and disordered knowledge of network community effectively and provide personalized service for users. We focus on analyzing the knowledge association among titles in an all-round way based on deep learning, so as to realize effective knowledge aggregation in academic virtual community. We take ResearchGate (RG) “online community” resources as an example and use Word2Vec model to realize deep knowledge aggregation. Then, principal component analysis (PCA) is used to verify its scientificity, and Wide & Deep learning model is used to verify its running effect. The empirical results show that the knowledge aggregation system of “online community” works well and has scientific rationality.