Title: Graph View-Consistent Learning Network. Abstract: Recent years, methods based on neural networks have made great achievements in solving large and complex graph problems. However, high efficiency of these methods depends on large training and validation sets, while the acquisition of ground-truth labels is expensive and time-consuming. In this paper, a graph view-consistent learning network (GVCLN) is specially designed for the semi-supervised learning when the number of the labeled samples is very small. We fully exploit the neighborhood aggregation capability of GVCLN and use dual views to obtain different representations. Although the two views have different viewing angles, their observation objects are the same, so their observation representations need to be consistent. For view-consistent representations between two views, two loss functions are designed besides a supervised loss. The supervised loss uses the known labeled set, while a view-consistent loss is applied to the two views to obtain the consistent representation and a pseudo-label loss is designed by using the common high-confidence predictions. GVCLN with these loss functions can obtain the view-consistent representations of the original feature. We also find that preprocessing the node features with specific filter before training is good for subsequent classification tasks. Related experiments have been done on the three citation network datasets of Cora, Citeseer, and PubMed. On several node classification tasks, GVCLN achieves state-of-the-art performance.