Network Optimization of Online Learning Resources from the Perspective of Knowledge Flow

Abstract

To effectively share and recommend knowledge, the online learning platform needs to deliver the most suitable and valuable learning resources to the demanders through the best path at a low cost. The existing studies mostly focus on the evaluation of knowledge flow ability and the extraction of knowledge classification features, but rarely tackle the knowledge flow evolution and network optimization in the light of the management of online learning resources (OLRs). To solve the problem, this paper explores the network optimization of OLRs from the perspective of knowledge flow. Firstly, the evolutionary game of the implicit knowledge flow in the OLR network was analyzed. In addition, an optimization model of OLRs was constructed, according to the evolutionary game mechanism for the implicit knowledge flow in the OLR network based on knowledge sharing, and the self-organizing hierarchical reconstruction. Finally, the network optimization effect was rated, and the network optimization was proved effective, with the management of music OLRs as an example.