Application of Apriori Algorithm in Professional Elective Course Data Mining for University

College students lack the purpose of professional elective course selection, and do not consider whether the course is helpful to future employment. In addition to the course content, teachers' engineering experience and course scheduling semester have an impact on the teaching effect of the course. It is biased to recommend students to choose professional elective courses only based on the experience of the dean or responsible professors. To solve this problem, we apply Apriori algorithm for elective course data mining to provide an objective evaluation. We takes students' course selection records and employment situation including further education as a transaction, and uses Apriori algorithm to give the strong association rules between professional elective courses and employment. Based on the data of one year's graduates, we recommend nine professional elective courses from sixteen courses to college students. This assisted the dean in his decision-making. In addition, it can also promote the continuous improvement of unselected courses.