Based on BP Neural Network Classification of Medical History of Traditional Chinese Medicine Literature Research

Hongmei Bao
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引用次数: 1

Abstract

Chinese traditional medical history literature contains the Chinese medicine, Chinese medicine document, doctrine of traditional Chinese medicine and ancient medical Chinese courses, and subject classification and the cultivation of professional talents cannot leave based on the literature materials. As a result, the category of the medical history of literature of traditional Chinese medicine for precise classification subject construction is the important factor. In this paper, some 660 species of Medical Medical history of Medical university library literature study, extracting the characteristics of word segmentation, build the sample set input index, the literature established categories (1 - skilled doctor, 2 - Medical books, 3 - Schools, 4 - Other) as the output, using the BP neural network algorithm to study the relationship between input and output tag. The simulation results show that the BP neural network algorithm is effective, fast, convenient and accurate prediction of the category, the medical history literature to verify the effectiveness of the algorithm.
基于BP神经网络的中医病史分类文献研究
中国传统医学史文献包含中医、中医文献、中医学说和古代中医课程,学科分类和专业人才培养离不开文献资料。因此,中医文献的医史范畴是进行精准分类学科建设的重要因素。本文以约660种医学史医学院校图书馆文献为研究对象,提取特征分词,构建样本集输入索引,将文献建立分类(1 -医术类、2 -医书类、3 -学校类、4 -其他类)作为输出,采用BP神经网络算法研究输入输出标签之间的关系。仿真结果表明,BP神经网络算法有效,预测类别快速、方便、准确,病史文献验证了算法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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