In recent years, tremendous efforts have been made to explore features contained in user-item graphs for recommendation based on Graph Neural Networks (GNN). However, most existing recommendation methods based on GNN use weighted sum of directly-linked node’s features only, assuming that neighboring nodes are independent individuals, neglecting possible correlations between neighboring nodes, which may result in failure of capturing co-occurrence signals. Therefore, in this paper, we propose a novel Graph Convolutional Network with Neighbor complex Interactions for Recommendation (GCNNIRec) focused upon capturing possible co-occurrence signals between node neighbors. Specifically, two types of modules, the Linear-Aggregator module and the Interaction-Aggregator module are both inside GCNNIRec. The former module linearly aggregates the features of neighboring nodes to obtain the representation of target node. The latter utilizes the interactions between neighbors to aggregate the co-occurrence features of nodes to capture co-occurrence features. Furthermore, empirical results on three real datasets confirm not only the state-of-the-art performance of GCNNIRec but also the performance gains achieved by introducing Interaction-Aggregator module into GNN.