摘 要：随着大数据时代的到来，传统数据库查询接口在处理复杂语义需求时面临诸多挑战，用户往往需要具备专业SQL技能才能高效获取所需信息。为此，本研究旨在设计一种基于语义分析的智能数据库查询接口，以降低用户使用门槛并提升查询效率。通过融合自然语言处理技术和语义解析算法，该接口能够将用户的自然语言查询转化为结构化查询语句，并结合上下文理解优化查询结果。实验结果表明，该接口在准确性和响应速度上均优于现有方法，特别是在处理多条件、跨表查询时表现出显著优势。本研究的主要创新点在于引入了深度学习模型以增强语义理解能力，并设计了一种动态调整机制以适应不同场景需求。这一成果不仅为用户提供了一个更直观的交互方式，也为智能数据库系统的进一步发展提供了新思路。
关键词：智能数据库查询接口；语义分析；自然语言处理
Abstract：With the advent of the big data era, traditional database query interfaces face numerous challenges in handling complex semantic requirements, as users often need professional SQL skills to efficiently obtain the desired information. To address this issue, this study aims to design an intelligent database query interface based on semantic analysis, which reduces the user threshold and improves query efficiency. By integrating natural language processing techniques and semantic parsing algorithms, the interface is capable of converting users' natural language queries into structured query statements while optimizing query results through contextual understanding. Experimental results demonstrate that the interface outperforms existing methods in terms of accuracy and response speed, particularly when dealing with multi-condition and cross-table queries. The main innovation of this study lies in the incorporation of deep learning models to enhance semantic understanding capabilities and the development of a dynamic adjustment mechanism to adapt to varying scenario requirements. This achievement not only provides users with a more intuitive interaction method but also offers new insights for the further development of intelligent database systems.
引言 1
一、语义分析基础与技术框架 1
(一)语义分析的基本原理 1
(二)关键技术与算法综述 2
(三)技术框架的设计原则 2
二、智能查询接口的需求分析 3
(一)用户需求的特征提取 3
(二)查询行为的模式识别 3
(三)接口功能的核心定位 4
三、数据库语义匹配机制设计 4
(一)数据结构的语义建模 4
(二)查询语义的解析方法 5
(三)匹配算法的优化策略 5
四、系统实现与性能评估 5
(一)系统架构的设计实现 6
(二)查询效率的实验分析 6
(三)用户体验的反馈改进 7
结论 7
参考文献 8
致谢 8
关键词：智能数据库查询接口；语义分析；自然语言处理
Abstract：With the advent of the big data era, traditional database query interfaces face numerous challenges in handling complex semantic requirements, as users often need professional SQL skills to efficiently obtain the desired information. To address this issue, this study aims to design an intelligent database query interface based on semantic analysis, which reduces the user threshold and improves query efficiency. By integrating natural language processing techniques and semantic parsing algorithms, the interface is capable of converting users' natural language queries into structured query statements while optimizing query results through contextual understanding. Experimental results demonstrate that the interface outperforms existing methods in terms of accuracy and response speed, particularly when dealing with multi-condition and cross-table queries. The main innovation of this study lies in the incorporation of deep learning models to enhance semantic understanding capabilities and the development of a dynamic adjustment mechanism to adapt to varying scenario requirements. This achievement not only provides users with a more intuitive interaction method but also offers new insights for the further development of intelligent database systems.
Keywords: Smart Database Query Interface；Semantic Analysis；Natural Language Processing
引言 1
一、语义分析基础与技术框架 1
(一)语义分析的基本原理 1
(二)关键技术与算法综述 2
(三)技术框架的设计原则 2
二、智能查询接口的需求分析 3
(一)用户需求的特征提取 3
(二)查询行为的模式识别 3
(三)接口功能的核心定位 4
三、数据库语义匹配机制设计 4
(一)数据结构的语义建模 4
(二)查询语义的解析方法 5
(三)匹配算法的优化策略 5
四、系统实现与性能评估 5
(一)系统架构的设计实现 6
(二)查询效率的实验分析 6
(三)用户体验的反馈改进 7
结论 7
参考文献 8
致谢 8
