基于决策树的图书馆文献资源智能检索方法

Intelligent Retrieval Method of Library Document Resources Based on Decision Tree

当前图书馆文献资源类型存在差异,检索过程中排序效果差,提出一种基于决策树的图书馆文献资源智能检索方法。智能检索中需要图书馆文献资源库去除噪声,获取有效数据,使用Skip-gram模型进行文字类资源特征提取,利用卷积神经网络作为图像文献资源的特征提取工具,提取过程采用Dropout机制随机断开部分网络连接,利用决策树从无规则次序数据库中推理出分类规则,计算属性与图书馆文献类型之间相关性,完成智能检索。测试结果表明:所提方法在不同数据集下的资源排序质量均优于传统检索方法。

At present,the types of library literature resources are different,and the sorting effect is poor in the retrieval process is poor.An intelligent retrieval method of library literature resources based on decision tree is proposed.Intelligent retrieval requires library literature resource library to remove noise,obtain effective data,it uses Skip-gram model for text resource feature extraction,uses convolutional neural network as a tool of image literature resources feature extraction,extraction process uses Dropout mechanism randomly disconnect part of the network connection,uses decision tree from irregular order database inference classification rules,computes the correlation between attributes and library literature type,completes the intelligent inspection a large rope.The performance test results show that the resource ranking quality of this method is better than the traditional retrieval methods in different data sets.