The propagation of information between nodes forms the foundation for social network collaboration. Identifying influential nodes to facilitate propagation in networks is essential for downstream tasks, such as social recommendations, scientific collaborations, and epidemic control. While previous research has explored most indicators associated with the topological structure of nodes to address this issue, little attention has been given to the fact that nodes have different ranges of influence. Thus, using predetermined ranges as the basis for measuring influence may not be the most suitable approach. Therefore, this paper introduces a method that utilizes graph neural networks to aggregate information from different hop neighborhoods. It incorporates learnable parameters to measure the importance of each neighborhood. Additionally, it employs von Neumann entropy to evaluate the purity of information within the neighborhood as a regularization term, thereby enhancing the performance of the semi-supervised regression model. The effectiveness and applicability of this method have been demonstrated in various networks, exhibiting remarkable accuracy. Furthermore, it enhances the interpretability of node influence prediction.