As a kind of Graph Convolutional Neural Network （GCNN）， Topology Optimization based Graph Convolutional Network （TOGCN） model adopts auxiliary information in the network to optimize topological structure of the network， thereby helping to reflect the relational degrees between the nodes. However， TOGCN model only focuses on the association between local nodes， and not enough on the potential global structure information. Fusing global feature information， the model will help to improve performance as well as its robustness in dealing with incomplete information. A Global structure information Enhanced-TOGCN （GE-TOGCN） model was proposed， the attributes of neighboring nodes were utilized to optimize the topological graph， and the class information was regarded as the global structure information to maintain intra-class aggregation and inter-class separation. Firstly， the center vector of each class was calculated by the labeled nodes， then some unlabeled nodes were selected to update these class center vectors. Finally， all the nodes were assigned to the corresponding class according to their similarity to class center vectors， and a semi-supervised loss function was adopted to optimize the class center vector of each class and the final representation vectors of the nodes. On Cora and Citeseer datasets， node classification task and node visualization task were performed by using the obtained node representation vectors with the loss of label information. Experimental results show that compared with Graph Convolutional Network （GCN）， Graph Learning-Convolutional Network （GLCN） and other models， GE-TOGCN has the classification accuracy increased by 1.2-12.0 percentage points on Cora dataset， and the classification accuracy increased by 0.9-9.9 percentage points on Citeseer dataset. In node visualization task， the proposed model has higher degree of intra-class node aggregation and more obvious boundaries between class clusters. In summary， the fusion of class global information can reduce the negative influence of label information loss on learning effects of the model， and the node representations obtained by the proposed model have better performance in downstream tasks.