Graph neural networks (GNNs) have attracted widespread attention due to their powerful modeling capabilities, and they are often used to solve node classification tasks on graphs. At this stage, the commonly used model with the graph convolutional network (GCN) as the core solves such problems. However, due to over-fitting and over-smoothing, the deep node embedding representation effect is not positive. Therefore, this study proposes a graph convolutional neural residual networks (GCNRN) model that combines residual connection and self-attention based on GCN kernel to improve the generalization ability of GCN. At the same time, in order to integrate more in-depth information, this study introduces a fusion mechanism, uses fuzzy integral to fuse multiple classifiers, and finally improves the model testing accuracy. In order to verify the superiority of the proposed method, this study uses ogbn-arxiv and commonly used citation datasets to conduct comparative experiments. Compared with many existing models with GCN as the core, the GCNRN model has an average improvement of node classification accuracy by 2% and avoids the traditional over-fitting and over-smoothing phenomena. In addition, the experimental results show that the multi-classifier model with the fusion module based on fuzzy integral has a better classification effect than the traditional fusion method.