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