基于机器学习的中文数据库自然语言检索系统

Natural language retrieval system of Chinese database based on machine learning

为了提升中文数据库自然语言检索能力,设计基于机器学习的中文数据库自然语言检索系统.首先对中文数据库自然语言的存储节点和规则集分布设计,构建中文数据库自然语言检索系统结构模型,然后结合模糊综合决策方法,进行中文数据库自然语言检索数据融合.以此为基础,采用联合线性相关融合的方法构建线性机器学习模型,并利用该模型进行中文数据库自然语言检索的特征聚类.最后根据特征聚类结果,采用机器学习算法,完成对中文数据库自然语言检索过程中的迭代融合和自适应控制,实现中文数据库自然语言的检索.测试结果表明,采用该方法进行中文数据库自然语言检索的查准率较高.

In order to improve the natural language retrieval capabilities of Chinese databases,a natural language retrieval system for Chinese databases based on machine learning is designed.Firstly,for the distribution design of the storage nodes and rule sets of the natural language of the Chinese database,the structural model of the Chinese database natural language retrieval system is constructed.Secondly,the natural language retrieval data of the Chinese database is fused which combined with a fuzzy comprehensive decision-making method.Based on the above work,a linear machine learning model is constructed based on a joint linear correlation fusion method,and the model is used to perform feature clustering for natural language retrieval in Chinese databases.Finally,according to the feature clustering results,the iterative fusion and adaptive control of the natural language retrieval process of the Chinese database are completed through the machine learning algorithm,and the retrieval function of the natural language of the Chinese database is realized.The experimental results show that the accuracy of natural language retrieval in Chinese databases using this method is higher than of traditional retrieval methods.