Medical tourism is experiencing an increase in demand due to the advancement of technology in the field of plastic surgery. Since the outbreak of COVID-19, there has been a dramatic decrease in the number of domestic medical tourists, leading to a crisis. By using big data analytics, this study aimed to identify the perception and trend of cosmetic medical tourism before and after COVID-19, as well as present strategic marketing information and basic marketing data to medical institutions. In this study, the Naver news data was analyzed and refined for three years before and after the outbreak of COVID-19, with the keywords of 'plastic surgery + medical tourism', as well as text mining to identify trends. The centrality analysis and CONCOR analysis were performed and visualized utilizing UCINET 6.0. Four clusters were created for the data before COVID-19: Tourism, Medical, Marketing, and Location, and after COVID-19, a COVID-19 cluster was added to make a total of five clusters. Prior to the outbreak of COVID-19, foreign countries' names were in high frequency, but after the outbreak of COVID-19, words associated with COVID-19, such as Corona, Quarantine, and Prevention, were in high frequency. On the basis of these results, suggestions were made based on the differences in cognition that existed from before and after the COVID-19.