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Graph representation learning technology aims to learn the low dimensional representation vectors for nodes while maintaining the properties of graphs and provide materials for downstream tasks. Most existing algorithms mainly focus on aggregating neighborhood features, lack the ability to capture non-linear information. To solve this issue, this paper proposes a graph convolutional network with adaptive fusion of neighborhood aggregation and interaction (AFAI-GCN). Firstly, a two-channel graph convolutional network is constructed to model the neighborhood aggregation, and the representations generated by modeling are used to calculate the neighborhood interaction items to enhance the learning capability of algorithm. Secondly, the attention mechanism is adopted in the adaptive fusion module to increase the attention on significant information and improve the task correlation of the fused information items. Finally, an information consistency constraint and a difference constraint are applied to enhancing node feature consistency and embedded representation difference. The node classification and visualization tasks are performed on three public citation datasets. Experiments results show that, compared with graph convolutional network (GCN), neighborhood aggregation and interaction graph convolutional network (AIR-GCN) and other algorithms, the classification accuracy of AFAI-GCN is increased by 1.0 to 1.6, 1.1 to 2.4, 0.3 to 0.9 percentage points on Cora, Citeseer and Pubmed datasets, respectively. In the visualization task, the degree of aggregation within clusters is higher and the boundaries of different clusters are clearer. Moreover, the convergence speed of AFAI-GCN is faster and the accuracy curve is smoother in the learning process. All these results indicate that AFAI-GCN is more advanced than the benchmark algorithms.