Existing recommender systems based on graph neural networks mainly aggregate the information of neighbors indiscriminately when updating the representation of the target node. In this way, most useful prior knowledge is not introduced in combination with the recommendation system itself to distinguish the relationship between users and items. To solve this problem, a Neighbor Relation-aware Graph Convolutional Network (NRGCN) is proposed, which combines three prior auxiliary information of rating, review text, and the timestamp to distinguish the expression differences of different neighbors in the neighborhood explicitly. Specifically, the user’s rating value is introduced as the basis for the closeness of the network, which is then modified by the sentiment rating of the review text. Besides, considering the changes in the user’s interest over time, the timestamp is used to encode the neighbor relationship at different times. Extensive experiments on three benchmark datasets show that our model outperforms various state-of-the-art models consistently, with a maximum increase of 12%.