Abstract: In order to gain a deeper understanding of users’ learning habits and development trends, and to dynamically adjust educational resources based on user needs and behaviors, we proposed a  dynamic evolution model of user learning behavior based on complex network clustering algorithm. Firstly, we designed a complex network clustering model to obtain the user learning behavior community. Secondly, we obtained  the distribution of data association rules through semantic binary analysis, and used  multiple regression methods to mine the association rules,  obtaining a user learning behavior feature distribution model. Finally, we obtained user learning behavior interest features, which were used as input to obtain a dynamic evolution model by adding  attention mechanism  to the gated recurrent unit network.  The experimental results show that the proposed method can effectively distinguish between behavior data that users in the learning community are interested in and not interested in. The AUC value is closer to 1, indicating that the proposed method has better performance and stronger practicality.

Key words: complex network, community excavation, data clustering algorithm, attention mechanism, analysis of learning behavior, dynamic evolution