Research on English teaching curriculum model based on MMSP algorithm

In order to analyse and extract teaching performance data, this study proposes a multi-dimensional and multi-layer association rule mining multi-dimensional sequential patterns (MMSP) algorithm for mining association rules between college English course learning and other courses. In the design of the MMSP algorithm, the Diffset strategy is used to mine subsets in the most orderly manner, the PrefixSpan algorithm is used to generate frequent sequences, and the mining of multidimensional frequent sequences is transformed into the mining of frequent fundamental vectors. The results indicate that single semester English course learning is influenced by professional and theoretical courses, and the learning situation of multi semester English courses has a certain degree of stability, which is related to the learning of public courses. The unique contribution of this study lies in considering the characteristics of teaching performance data and designing frequent multidimensional sequences to include different levels of hierarchical granularity.