Page 8 - 《软件学报》2026年第3期

软件学报 ISSN 1000-9825, CODEN RUXUEW E-mail: jos@iscas.ac.cn
2026,37(3):971−1005 [doi: 10.13328/j.cnki.jos.007516] [CSTR: 32375.14.jos.007516] http://www.jos.org.cn
©中国科学院软件研究所版权所有. Tel: +86-10-62562563

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向量数据库中近似最近邻搜索关键技术综述

宋子文, 王 斌, 张喜瑞, 赵世豪, 杨晓春

(东北大学 计算机科学与工程学院, 辽宁 沈阳 110819)
通信作者: 王斌, E-mail: binwang@mail.neu.edu.cn

摘 要: 高维向量近似最近邻搜索 (approximate nearest neighbor search, ANNS) 是向量数据库的基础和核心之一.
随着人工智能的发展, 向量数据库发挥了日益关键的作用, 获得了广泛的关注, 高效的 ANNS 方法对向量数据库的
性能优化十分关键. 在几十年的发展中, ANNS 取得了一系列成果. 近些年随着该领域的快速发展, 涌现出来的新
方法和研究成果亟须系统性梳理. 首先介绍了 ANNS 的基本概念; 其次在已有的综述框架的基础上, 根据向量数据
组织方式将当前的内容进一步归纳为基于图、层次、量化、哈希和混合数据组织这 5 类, 并结合代表性和最新的
成果进行介绍; 然后从向量数据搜索优化方法的角度提出面向硬件加速、面向学习增强、面向距离比较操作、面
向磁盘内存混合场景、面向数据访问优化、面向分布式场景、面向混合查询和理论分析这 8 个方面的分类体系
对最近的搜索方法进行综述; 最后基于当前的研究成果和趋势, 展望未来的研究方向.
关键词: 近似最近邻搜索; 向量数据库; 索引; 高维数据; 向量搜索; 查询优化
中图法分类号: TP311

中文引用格式: 宋子文, 王斌, 张喜瑞, 赵世豪, 杨晓春. 向量数据库中近似最近邻搜索关键技术综述. 软件学报, 2026, 37(3):
971–1005. http://www.jos.org.cn/1000-9825/7516.htm
英文引用格式: Song ZW, Wang B, Zhang XR, Zhao SH, Yang XC. Survey on Key Techniques of Approximate Nearest Neighbor
Search in Vector Databases. Ruan Jian Xue Bao/Journal of Software, 2026, 37(3): 971–1005 (in Chinese). http://www.jos.org.cn/1000-
9825/7516.htm

Survey on Key Techniques of Approximate Nearest Neighbor Search in Vector Databases

SONG Zi-Wen, WANG Bin, ZHANG Xi-Rui, ZHAO Shi-Hao, YANG Xiao-Chun
(School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China)
Abstract: High-dimensional approximate nearest neighbor search (ANNS) is one of the fundamental and core components of vector
databases. With the advancement of artificial intelligence, vector databases have played an increasingly critical role and have gained
widespread attention. ANNS methods are essential for optimizing the performance of vector databases. Over decades of development,
ANNS has achieved a series of milestones. Rapid advancements in this field in recent years have led to a surge of novel methods and
findings, necessitating systematic organization. In this study, the basic concepts of ANNS are first introduced. Next, building upon existing
survey frameworks, current approaches are further categorized into five groups based on vector data organization methods: graph-based,
hierarchical, quantization-based, hashing-based, and hybrid data organization. Representative works and the latest research advances in the
field are systematically discussed. Then, from the perspective of vector search optimization methods, recent advancements are reviewed and
categorized into eight types. These categories include hardware acceleration oriented, learning enhanced, distance comparison operation
oriented, disk-memory hybrid oriented, data access optimization oriented, distributed oriented, hybrid query oriented, and theoretical
analysis. Finally, based on current research achievements and trends, potential future research directions are outlined.

* 基金项目: 国家重点研发计划 (2024YFF0617702); 国家自然科学基金 (U22A2025, 62232007, U23A20309); 高等学校学科创新引智计划
(B16009)
本文由“向量数据库及 DB4LLM 技术”专题特约编辑高宏教授、李国良教授、张蓉教授推荐.
收稿时间: 2025-05-06; 修改时间: 2025-06-30; 采用时间: 2025-08-20; jos 在线出版时间: 2025-09-02
CNKI 网络首发时间: 2026-01-15