Relied on big data technology to analyze the learning data of prerequisite courses，the curriculum team developed four types of student portraits，namely the passive type，the diligent type，the moderately performing type，and the all-round excellent student type. Through identifying genuine teaching issues faced by students of different portraits in the Clinical Nursing of TCM，the team initiated precision teaching reforms tailored to these students’ profiles. Before the class，the learning groups were precisely divided，students’ academic conditions were accurately diagnosed，and customized learning objectives were set. During class，precise interactions were conducted，and three types of progressive cases were designed. After class，classified learning tasks was designed to meet the learning needs of students with different portrait types. The practice has found that，as the data hub of precision teaching，student portraits can transform students’ learning data into an effective basis for teachers to adjust their teaching strategies. This is helpful for solving the contradiction between large-scale teaching and personalized teaching，and promoting the transformation of the teaching paradigm from subjective empiricism to objective dataism.