信息资源管理学报 ›› 2019, Vol. 9 ›› Issue (1): 21-29.doi: 10.13365/j.jirm.2019.01.021

• 专题-智慧养老研究 • 上一篇    下一篇

2013—2017年我国养老政策量化研究

何振宇 白玫 朱庆华   

  • 收稿日期:2018-10-08 出版日期:2019-01-26 发布日期:2019-01-26
  • 作者简介:何振宇,男,博士研究生,研究方向为网络信息资源管理,Email:demandsupply@163.com;白玫,女,博士研究生,研究方向为智慧养老;朱庆华,男,博士,教授,研究方向为网络信息资源管理、互联网用户行为、健康信息学等。

Quantitative Analysis of China’s Pension Policy from 2013 to 2017

He Zhenyu Bai Mei Zhu Qinghua   

  • Received:2018-10-08 Online:2019-01-26 Published:2019-01-26

摘要:

分析我国养老政策的核心要点,为智慧健康养老产业的发展提供借鉴与参考。首先通过量化研究的方法,梳理养老政策的主要脉络,其次通过TF-IDF算法从目标政策文件中提取高频关键词,采用共词分析对提取的高频关键词进行聚类,并针对聚类结果展开分析。最后总结了我国养老政策的几个核心类别,为养老体系建设提出了几点建议。本文高频关键词的选取采用了人工筛选的方式,可能会存在错漏;共现算法使用ochiia系数,无法区分共现中特征词的频度。

关键词: 养老政策, 共词分析, TF-IDF算法, 高频关键词, 智慧养老, 养老产业

Abstract:

This paper analyzes the core points of China's pension policy and provides reference for the development of the smart healthy and pension industry. First it combs the main thread of pension policy through quantitative research method. Then, TF-IDF algorithm is used to extract high-frequency keywords from target policy documents. And through the co-word analysis, the extracted high-frequency keywords are clustered and analyzed according to the clustering results. Finally, it summarizes several core categories of China's pension policy, and puts forward several suggestions for the construction of the pension system. The selection of high-frequency keywords has been artificially screened, therefore there may be errors or omissions. The co-occurrence algorithm uses the ochiia coefficient, and it can not distinguish the frequency of feature words in co-occurrence.

Key words: Pension policy, Co-word analysis, TF-IDF algorithm, High-frequency keywords, Smart pension, Pension industry

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