Optimization Design of College Teaching Reform Paths in the Context of Big Data Mining-Driven High-Quality Development of Commerce and Circulation Based on Big Data Mining

Abstract At present, many colleges and universities apply data mining technology to the optimal design of the teaching path, which can analyze and process the singularized data through mining technology means and discover the valuable information therein, which not only reduces the time of analyzing the data to a large extent, but also improves the utilization rate of the data. The article first provides a systematic overview of factor analysis, and then uses the factor analysis model to monitor the quality of the teaching of commerce and distribution majors, and discovers the deficiencies of current college teaching based on the experimental results. The ideas and measures for practical teaching reform are explored on the basis of evaluation and analysis. Subsequently, the article studied the optimization of k-mean clustering algorithm based on the fireworks algorithm, and took the results of commerce and circulation majors of students in a university as the research data, and used the cluster analysis method to carry out empirical research. In the test experiment of the between-subjects effect of comparing data structure and database principles courses, it was found that with the deepening of innovative activities, the before and after comparison of the two courses of data structure and database principles, the students who enrolled in different years had a significant difference in learning results with other students with Sig>0.05.