The user persona is a sketch and description of the user image, which has been widely used in typical retail scenarios such as the wake-up of sleeping members, prediction of users arriving at the store, and personalized recommendations. Drugs are different from ordinary commodities, and they contain strong semantic knowledge. The existing user persona mainly starts from the consumption attribute and static attribute and is not completely applicable to the pharmacy marketing and prediction field. This study proposes a persona of pharmacy user (UPP) model for the drug field, which embeds medical knowledge on the existing persona and uses methods such as rules, clustering, statistics, and entity recognition to extract new labels including chronic diseases, diseases, special diseases, activity sensitivity, user value, and price preference. All labels are integrated into a clustering-based group division method to form the user profile. The experiment shows that the accuracy of this model is 13% higher than the existing user persona model in the consumer behavior prediction scenario, so the proposed model is more suitable for the pharmacy marketing scenario.