Traditional session recommendation mainly uses the time sequence of users clicking items to construct a user session graph, which often ignores the similarity and differences between user groups. To improve the effect of recommendation, an E-SGNN (E-SGNN, Edge-Session Graph Neural Network) method combining edge information clustering and session recommendation is proposed. Firstly, similar users are clustered by edge information and divided into different session user groups. After extracting the data features of …