# A Graph Neural Network Node Classification Application Model with Enhanced Node Association
## 1 Introduction  
In recent years, with the advancements in computer technology, artificial intelligence, and other related industries, there has been a growing focus on graph neural network algorithms based on deep learning. The development stages of graph neural networks have led to unprecedented progress in the learning of graph structure. One of the most common tasks is semi-supervised node classification [1, 2], in which many nodes lack corresponding labels, and the labels of unlabeled nodes must therefore be determined using the topological complementary information of the nodes in the graph [3]. Social network analysis, protein interoperation network analysis, recommender systems, inter-document classification, and inter-paper classification [4] are just a few of the fields in which GCN's semi-supervised node classification has been widely applied. Node classification based on graph neural networks has yielded outstanding results in previous research, and these methods boil down to the aggregation of features using the graph structure (the corresponding adjacency matrix is indirectly understood as the presence of edge connections) [5]. Moreover, among the existing classification models for graph neural networks, the existing models are too homogeneous to simultaneously account for the types of problems each would bring under various conditions [6, 7]. This paper proposes an innovative graph neural network node classification model named ENode-GAT. Through a review of relevant literature and materials, we enhance the interconnectivity between nodes by integrating the classification properties of the graph neural network model andthe inherent connectivity of the graph structure formed by the dataset prior to performing convolution computation in the convolution layer.. It is also confirmed that this model can be applied in the future to duties such as the classification of papers, stocks, tender documents, and announcements issued by various local governments. Unlike the conventional node classification model, this model reconstructs the graph structure prior to the calculation of convolution by introducing external nodes of similar terms. This process focuses on the effect of virtual edge relationships between nodes because a large number of nodes are in relative isolation and the graph structure required by the model is unconnected, which can have a significant impact on the experimental results [8; 9]. Take, for instance, the graph structure of a citation network, in which the vertices represent individual papers, and the edges represent the citation relationships between the papers. Evidently, a paper cites another paper with the same topic, but a paper cannot cite all related papers, which would demonstrate that edge relations play a significant role in the node classification task. A node classification task appears to require a focus on the edge relationship between nodes, and it can be demonstrated that the model proposed in this paper has high application value. In this paper, a model applicable to the node classification of graph neural networks is proposed to resolve the shortcomings of the existing node-level classification model of graph neural networks. This model employs a graph convolutional neural network coupled with a graph attention mechanism and incorporates an early stop algorithm when overfitting occurs to finish the classification model design, which is used to finish semi-supervised node classification in graph neural networks. The feasibility of the model is confirmed by the common dataset as well as the small sample dataset that is established. The following are the principal contributions of this paper:  
* ENode-GAT is a node classification model for graph neural networks that improves the connections between nodes.