This study deals with constructing knowledge maps using social network analysis, which is one of the data analytics techniques. A total of 892 scientific journals related to tea beverage drinking were used as the raw data to build two knowledge maps. One map showed the association between the plant raw material and phytochemical ingredients, while the other presented the association between the phytochemical ingredients and the pharmaceutical efficacy. The two knowledge maps used hierarchy to identify the relationship between the plant raw material and its pharmaceutical efficacy and applied the concept of centrality and cohesion. Data analysis indicated that the tea (Camellia sinensis), citrus, dandelion plants showed a high centrality and predominance. Furthermore, the phytochemical ingredients with a high centrality were phenols, flavonoids, and vitamin C. Pertaining to the pharmaceutical efficacy, the antioxidant, anti-inflammatory, and antimicrobial functions showed a high centrality. Therefore, this study confirmed the usefulness of tea ingredients through the knowledge maps linking the plant raw material, phytochemical ingredients, and pharmaceutical efficacy. Our findings provided insights into future directions for tea product development in the food industry. Key words:raw material tea plant, pharmacological efficacy, social network analysis, knowledge map, data analytic