摘要： 针对现有学术文献分类方法忽视文献数据之间关联信息的问题，提出了一种融合图卷积网络（GCN）和对比学习的文献分类模型对比图卷积网络（CGCN）。首先基于文献内容和引用关系定义两类“同质—异质”关联信息，并将其转换为构建对比损失的自监督信息；然后，利用对比损失优化GCN特征提取过程，推动同质文献特征表示彼此接近、异质文献彼此远离；最后，利用交叉熵损失和softmax函数实现“端到端”的学术文献分类。在三个基准学术文献数据集上，CGCN的文献分类表现优于当前较为先进的基线模型，特别是Cora数据集上Micro-F1和 Macro-F1指标值相较原始的GCN模型提高8.29%和7.91%。CGCN通过基于“同质—异质”关系构建的对比损失，增强了模型对文献数据潜在信息的表征能力，提高了分类的准确性和泛化性，为学术文献分类研究提供了新思路和新方法。

Abstract: In order to solve the problems of current academic paper classification methods, which neglect the relational information, we propose a novel classification model that integrates Graph Convolutional Networks (GCN) with contrastive learning, called Contrastive Graph Convolutional Network (CGCN). Firstly, we define two distinct types of homogeneous-heterogeneous relational information based on the content and citations of the papers, transforming these into self-supervised information for constructing the contrastive loss. Secondly, we enhance the feature extraction process of GCN by employing contrastive loss, pushing homogeneous papers to be close to one another while ensuring that heterogeneous papers remain distant. Thirdly, we utilize cross-entropy loss and the softmax function to complete end-to-end academic paper classification. On three benchmark academic datasets, the CGCN outperformed advanced baselines in classification task. Micro-F1 and Macro-F1 are raised by 8.29% and 7.91% respectively compared to the original GCN on the Cora dataset. CGCN enhances the capacity to represent potential information in papers by employing a contrastive loss based on the homogeneous-heterogeneous relationship, thereby improving prediction accuracy and generalization. This approach provides innovative ideas and methods for research in academic paper classification.