English Composition Assisted Writing Training Based on Big Data Text Mining

With the wide application of computer multimedia technology in university English language teaching (EL T)., the automatic evaluation system (AES) of English composition has become the breakthrough point for English teachers to reform ELT means and methods. Because of the openness of the composition itself and the subjectivity of the marking staff in the marking process., it is always difficult to ensure the consistency of the marking standards. This article proposes an automatic assessment algorithm for English composition based on big data text mining, which provides theoretical model support for the development of intelligent auxiliary tools for English composition training. The simulation results show that the accuracy of this algorithm has obvious advantages, reaching more than 93%, compared with the support vector machine (SVM) algorithm and the random forest (RF) algorithm. Therefore, the automatic essay evaluation (AEE) model based on big data text mining is a reasonable and feasible assessment model, which is of great significance for the innovation of AEE system. Using this model to effectively assist manual assessment can truly test students' practical language application ability, assist students' English composition writing training and help students effectively improve their writing ability.