With the rise of deep learning and the development of graph neural networks, the problem of node classification based on graph data has gained widespread application in the real world. Gradually, semi-supervised node classification of graph neural networks has emerged as a primary focus of research. In recent years, graph-based semi-supervised learning has been a popular area of study, where learning can be performed with a small number of labels by exploiting the graph or stream structure of the data. Li et al. [
20] validated the benefits of semi-supervised learning of graph convolutional neural networks, trained the graph convolution using joint training and self-training methods, and achieved good classification results for semi-supervised classification of graph convolutional neural networks with fewer labels, thereby validating their proposed theory. Hu et al. [
21] proposed a new deep hierarchical graph convolutional network (H-GCN) for semi-supervised node classification on a common node classification dataset. The proposed method outperformed the then-current state-of-the-art methods and improved the accuracy by 5.9% with a small sample size, which served as a good inspiration for the subsequent researchers. On the study of node classification, Zeng et al. [
22] introduced the current characteristics of node classification in graph neural networks and proposed a node embedding enhancement model along with a comprehensive analysis of the graph structure of graph neural networks. By introducing the DSM model and mining the concealed connections between nodes, extensive experiments were conducted on a public dataset, and the results demonstrated that the proposed model was superior to the conventional model. At this juncture, Guo et al. [
23] proposed an integrated model of Bagging to address the issue of unbalanced classification of graph neural network nodes. The primary classifier of the model is a graph convolutional neural network, and majority voting is used to complete the integration. Extensive experiments demonstrate that the model is capable of classifying nodes with an imbalance. Jang et al. [
24] combined graph neural networks and natural language processing flawlessly to study an RF-EMF model of radio frequency electromagnetic field in order to complete the classification of scientific literature. They obtained promising experimental results and demonstrated the future applicability of the technology to text classification. Gong et al. [
25] discovered that graph edge features contain important information, and in this paper, a new framework for partial graph neural networks is developed to enable them to conduct focused computations on graph edge features. In addition to graph node classification of multiple citation networks, the new model is also applied to full graph classification and regression of multiple molecular datasets. It is shown that the proposed model outperforms the then-advanced graph neural network classification models, demonstrating the significance of edge features in graph neural networks. Yang et al. [
26] propose a novel model for node and edge feature learning in graph neural networks based on a hierarchical two-layer attention mechanism. They note that most of the state-of-the-art graph learning methods to date only focus on node features, ignoring rich relationship information contained in edge features. The authors demonstrate that node and edge embeddings can mutually enhance each other by training the model on different datasets and validating its feasibility. CensNet, the edge-node switching convolutional graph neural network, was proposed by Jiang et al. [
27] for semi-supervised classification and regression of graph-structured data with node and edge features. CensNet is a general framework for graph embedding that embeds nodes and edges within a latent feature space. Experimental findings on realistic academic citation networks and quantum chemical maps demonstrate that the method performs at or near the cutting edge. Xiao et al. [
28] proposed a hypergraph convolutional neural network called HGCNN for the classification of irregular citation network data at the node level. Unique advantages are provided by this model, which converts feature vectors of nodes into predicted labels and validates them on multiple datasets. Huang et al. [
29] enhanced the classification of citation networks by employing the knowledge of graph neural network node classification and powerful deep learning techniques to achieve improved classification results. The classification of nodes for graph neural networks is successfully accomplished. Qiang et al. [
30] classified the tender documents using the small sample classification of graph neural networks. This study makes extensive use of node classification knowledge and an effective graph neural network model to achieve the desired results. This experiment will serve as an invaluable resource for future researchers in this field. Xu et al. [
31] effectively introduced graph neural networks to the financial industry and completed the classification of small sample nodes for graph neural networks, paving the way for graph neural networks to be implemented across different domains. Li et al. [
32] effectively extended the graph neural network node classification problem to course learning, which is a significant breakthrough and a new application area for the future growth of graph neural networks.
ENode-GAT is a novel graph neural network node classification model introduced in this study. The model uses external similar word node referencing to strengthen the connections between nodes and reconstruct the graph structure, thereby achieving the classification task for nodes. After experimental validation on the Cora dataset and the self-developed Stock dataset, the ENode-GAT model exhibits accurate and efficient classification results. In the future, the model will be extended to several emerging fields, such as tender document classification, news classification, and government announcement classification, in order to improve the precision and dependability of this type of classification task.