Research on the Application of Improved Apriori Algorithm in College English Teaching

This paper utilizes the improved Apriori algorithm to mine the information of college English teaching. Firstly, in view of the limitation of traditional Apriori algorithm, weighted impact factor is used to prune the candidate item set, so as to reduce the scanning times of transaction database and solve the problem that the candidate item set is too large. Then, the improved Apriori algorithm is used to mine the information data of college English teaching. Finally, the effect of Apriori algorithm before and after improvement in mining the information of English teaching is compared. The results show that the improved Apriori algorithm can be used to mine the information of college English teaching, and the mining performance is better than the classical ID3 algorithm. The confidence of the improved Apriori algorithm in mining the association rules of teaching information mining is between 78% and 83%, and the accuracy is between 80% and 85%, which is higher than the traditional ID3 algorithm. Therefore, it is concluded that the improved Apriori algorithm can more accurately mine the association between information and data of college English teaching, so as to provide data reference for the improvement of English teaching quality.