To better cultivate talents in the direction of “Microbiological Pharmaceutical Technology”. In this paper, we first mine the Civic Elements from the Microbial Pharmaceuticals course with the help of a clustering algorithm and calculate the similarity between data objects by Euclidean distance. Secondly, the Apriori algorithm is applied to find the largest set of frequent items in the data set of Civic Science elements and analyze them. Then the ID3 decision tree is used to calculate the information gain involving conditional entropy and information entropy, and then the data are classified. Based on the above algorithm, a Civics teaching platform combined with theoretical and practical teaching is constructed. Finally, to verify that the platform constructed in this paper achieves the combination of theoretical teaching and practical teaching, a set of simulation experiments is designed in this paper, taking “Microbial Pharmaceutical Technology” as an example, and the results show that applying the Civics Teaching Platform to the microbial pharmaceutical course, the number of teachers Civics teaching elements mining increases from 14 to 22, and the ability of Civics elements mining increases significantly. Thus, it can be seen that the implementation of the Civic Government teaching platform based on the big data algorithm of the course has improved the teaching ability of teachers and is conducive to the realization of the talent cultivation goal of the course “Microbial Pharmaceutical Technology”.