由于噪声（如广告）、权限不足、隐私保护或恶意伪装等原因，造成大量网页显式内容特征不能被及时、全面的获取。在此背景下，为解决在网页内容特征严重缺失情况下如何对网页有效分类的问题，提出一种基于图嵌入与集成分类算法XGBoost（extreme gradient boosting）利用网页链接网络中隐含关系特征进行网页多分类的方法。首先，利用网页及网页间的超链接关系，构造出网页链接网络；然后，通过图嵌入模型抽取节点（网页）在链接网络中的隐含关系特征；其次，提取节点的集聚系数、PageRank值等统计学结构特征，共同构成节点的稠密特征向量；最后，利用基于XGBoost等集成学习模型构建节点分类预测模型对网页进行分类预测。在真实维基百科网页链接数据集上的实验结果表明：在完全缺乏网页显式内容特征情况下，所提出的Struct2Vec * + XGBoost组合方案实现了良好的网页分类效果，在准确率、精准率、查全率及F₁值4项指标上分别达到0.987 5、0.965 9、0.971 3和0.964 1。

Explicit content features of webpages are often unavailable due to distractions such as commercials, insufficient permissions, privacy protection, or deceptive disguises. To address the challenge of classifying webpages with severe content feature deficiency, a method combining graph embedding and extreme gradient boosting(XGBoost) was proposed. This method leveraged implicit relational features in webpage hyperlink networks for multi-classification. Firstly, a hyperlink network was constructed using relationships between webpages. Then, node features were extracted using graph embedding models, and statistical structural features such as clustering coefficients and PageRank values were concatenated to form dense feature vectors. Finally, ensemble learning models, including XGBoost, were trained to classify webpages for prediction. Experiments on a real Wikipedia dataset show that the Struct2Vec * +XGBoost approach achieves excellent classification results, with accuracy, precision, recall, and F₁-score metrics reaching 0.987 5, 0.965 9, 0.971 3, and 0.964 1, respectively. These results are superior to those of comparison models. The findings demonstrate the effectiveness of using implicit link-based features for webpage classification in scenarios with content feature deficiency.