基于趋势度分析的科技领域核心主题发展预测  

Forecasting Developments of Core Topics in Science and Technology with Trend Analysis

作　　者：崔骥 张金鹏 包舟 丁晟春[1] Cui Ji;Zhang Jinpeng;Bao Zhou;Ding Shengchun(School of Economics and Management,Nanjing University of Science&Technology,Nanjing 210094,China)

机构地区：[1]南京理工大学经济管理学院,南京210094

出　　处：《数据分析与知识发现》2022年第9期1-13,共13页Data Analysis and Knowledge Discovery

基　　金：江苏省社会科学基金项目(项目编号:20TQB004)的研究成果之一。

摘　　要：【目的】生成基于趋势度的预测模型,对领域文献进行分析,从而预测领域核心主题的发展。【方法】分析科技文献表征其研究主题的特征项,进行主题识别,构建战略坐标识别领域核心主题及时间序列,利用ARIMA模型和指数平滑法预测主题的趋势度。【结果】指数平滑法预测结果的平均绝对误差与平均均方根误差均小于ARIMA模型。【局限】模型中的初始参数选取、系数分布以及文献发文量的分布会对预测效果造成影响。【结论】两种模型对于增长型主题和新兴型主题有较好的预测效果,指数平滑法的预测精准度优于ARIMA模型。[Objective]The study creates a predictive model based on trending topics and analyzes the related literature,aiming to forecast the developments of core topics.[Methods]First,we analyzed the characteristics of research topics from scientific and technological literature.Then,we extracted the core topics of strategic coordinate identification.Finally,we used the ARIMA model and exponential smoothing method to predict the topics’trending degrees.[Results]The mean absolute error and mean root mean square error of the exponential smoothing method were both smaller than those of the ARIMA model.[Limitations]The selection of initial parameters for the model,the distribution of coefficients and the number of published papers will affect the prediction performance.[Conclusions]The two proposed models could yield better prediction results for growing and emerging topics.

关 键 词：主题发现 VOS聚类 指数平滑法 ARIMA模型 战略坐标 

分 类 号：G350[文化科学—情报学]