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论文编号：	10084
作者编号：	2120162787
上传时间：	2018/6/8 9:56:24
中文题目：	我国数字图书馆研究文献的发展趋势预测与分析
英文题目：	Prediction and Analysis of Development Trends of Research Literature in Digital Library in China
指导老师：	李培
中文关键字：	融合方法；数字图书馆；趋势预测；共词聚类
英文关键字：	fusion method ; digital library ; trend forecasting; co-word clustering
中文摘要：	随着科学技术的发展以及研究的不断深入，用户对数字图书馆的信息需求和研究者对数字图书馆的研究主题都在发生着重要的变化。为了满足广大用户的信息需求、更好的发挥数字图书馆的作用，需要对数字图书馆发展的现状及趋势进行分析，这也是顺应时代发展，把握整体发展动向，提升研究质量的重要途径。 然而，在当前的数字图书馆发展趋势预测与分析研究中，往往是根据人的主观判断进行的定性分析，研究预测出一个大概的范围，研究者根据自己的经验进行分析，研究方法的准确性及科学性较差。同时，运用一种分析方法也不能全面解释文献的主题内容。本研究正是在这种情况下，运用信息融合的方法对文献发展趋势进行预测，并采用较为成熟的共词聚类分析法进行主题内容分析。本文收集了2002年到2017年十六种期刊刊载“数字图书馆”主题的文献，首先以2002-2015年文献量为研究对象，运用灰色预测分析法、一元线性回归分析法、时间序列分析法进行预测分析，分别预测2016-2020年研究文献的发展趋势。然后通过融合方法将三种预测方法的预测结果进行综合处理，得到基于融合方法的预测结果，并将2016年和2017年的预测结果与实际文献量进行对比验证。然后运用社会网络分析法和系统聚类分析法对文献的高频关键词进行共词聚类分析。最后对我国数字图书馆研究文献的发展趋势进行综合分析。结果表明：融合方法能够修正单一预测方法的缺陷，并能较科学的预测数字图书馆研究文献量呈下降的发展趋势；而根据关键词共现以及聚类分析，表明了数字图书馆研究领域不断拓宽、深化，同时也标志着该领域研究已进入成熟期，相关研究从量向质转变。 研究在分析数据、总结结果的基础上，指出了数据收集“不全面”、融合算法缺乏深入探究以及共词聚类方法的不足，进而对未来研究方向作出展望。同时，研究说明了融合方法在文献发展趋势预测领域应用具有显著效果的意义，指出了运用融合方法进行文献预测和共词聚类进行分析相结合的创新之处。本研究图10幅，表15张，参考文献63篇。
英文摘要：	With the development of technology and the deepening of research, users have made important changes to the information needs of digital libraries and researchers have made important changes to the research direction of digital libraries. In order to meet the needs of users and better play the role of digital libraries, it is necessary to analyze the status quo and trends of the development of digital libraries. This is also an important way to follow the development of the times, grasp the overall development trends, and enhance the quality of research. However, in the current digital library development trend forecasting and analysis research, qualitative analysis based on human subjective judgment is often performed. The research predicts an approximate range, the researcher analyzes based on his own experience, and the accuracy and scientificity of research methods are poor. At the same time, the use of an analytical method cannot fully explain the subject matter of the literature. It is in this case that this research is based on the information fusion method to predict the development trend of the literature, and adopts a more mature co-word clustering analysis method to analyze the subject content. This article collected the literature on the topic of “Digital Libraries” published in sixteen journals from 2002 to 2017. First, the volume of documents from 2002 to 2015 was used as the research object, and grey prediction analysis, one-dimensional linear regression analysis, and time series analysis were used. Predictive analysis was conducted to predict the development trends of research literature from 2016 to 2020. Then, the forecasting results of the three forecasting methods are comprehensively processed by the fusion method, and the forecasting results based on the fusion method are obtained, and compared with the actual literature amounts in 2016 and 2017. Then the social network analysis method and system cluster analysis method are used to co-word cluster analysis of high frequency keywords in the literature. Finally, it comprehensively analyzes the development trend of the research literature in China's digital library. The results show that the fusion method can correct the defects of the single prediction method and can scientifically predict the development trend of the literature. According to the co-occurrence of keywords and cluster analysis, it shows that the research field of digital library has been continuously expanded and deepened. It also indicates that research in this field has entered a mature period, and related research has changed from quantity to quality. The research points out two main shortcomings, the “incomplete” of data collection and fusion algorithms lack in-depth research, which leads to the direction of future research, with the foundation of analyzing data and concluding the results. Meanwhile, the research shows that the fusion method has a significant effect in the field of literature development trend prediction and the innovation of this paper combines the use of fusion methods for document prediction and research on subject group analysis. This study includes 10 graphs, 15 charts and 63 references.
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