This study intends to identify the meaning and information inherent in text documents on the elderly movement in the Pandemic era through key keywords using semantic network analysis, and to explore the relationship between the constructs. To this end, after the Corona19 outbreak, online text data such as news and social media were collected, and 50 major key words were extracted based on the frequency of high co-occurrence words through a stagnant process three times. Key word frequency analysis and matrix data were extracted through TEXTOM for data collection and analysis, and network analysis and equivalence structure analysis were performed using UCINET6 and NetDraw programs. According to the results of the analysis, first, the key words with the most co-occurrence word frequency were health, public facilities for the elderly, muscle strength, dementia, and prevention in order. Second, in connection centrality, proximity centrality, and mediation centrality, the key word of the highest centrality in common was health. Third, a cluster intensively connected to social support, mental health, physical health, and exercise practice factors was formed. Therefore, this study has important significance in understanding and examining the social perception of the elderly movement in the pandemic era through semantic network analysis. It can be used as a basic data for establishment.