实体统一对于提高数据的简洁性和准确性具有重要意义。在Web数据集成领域,实体统一是被广泛关注的重点研究方向。文章基于朴素的成对匹配实体统一算法,提出一种新的基于特征的增量式实体统一方法,通过对实体特征的有效区分并定义新的数据结构,提升了算法的准确率,降低了算法时间复杂度,并应用于DBLP论文数据库,结果证明该方法可以有效提升Web数据集成的数据质量。 Entity reunification is of great importance for improving the conciseness and accuracy of the data. In the field of Web data integration, entity reunification is a key research direction that has been widely concerned. Based on the naive pairwise matching entity matching algorithm, this paper proposes a new feature-based incremental entity unification method. By effectively distinguishing the entity features and defining the new data structure, the algorithm improves the accuracy of the algorithm and reduces the cost of the algorithm Time complexity and apply to DBLP dissertation database. The result proves that this method can effectively improve the data quality of Web data integration.