The Intelligent Evaluation Framework of Professional English Composition Based on Semantic Similarity and XGBoost Algorithm

In order to meet the challenges of the research on the intelligent evaluation framework of professional English composition, in view of the shortcomings of the existing dynamic programming algorithms, this study introduces an innovative research method of the intelligent evaluation framework of composition based on semantic similarity and XGBoost algorithm. This new approach uses iterative finding of the best theoretical principles to pinpoint and locate key influencing factors, and accordingly makes a wise classification of indicators to reduce potential interference. At the same time, by using the unique mechanism of semantic similarity and XGBoost algorithm, this scheme cleverly constructs a design strategy for intelligent evaluation of English composition. The empirical results show that this scheme shows a significant improvement compisd with the traditional dynamic programming algorithm in terms of the accuracy of the composition intelligent evaluation framework and the processing efficiency of key factors, showing its obvious strong advantages. In professional English composition, the research on the intelligent evaluation framework of composition plays a crucial role, which can accurately predict and optimize the growth trend and output results of the research on the intelligent evaluation framework of professional English composition. However, in the face of complex simulation tasks, traditional dynamic programming algorithms show some inherent shortcomings, especially when dealing with multi-level challenges, their performance is often unsatisfactory. To overcome this problem, this study introduces a new idea of semantic similarity and XGBoost algorithm optimization of the composition intelligent evaluation framework, and uses the best theory to accurately control the influencing parameters through iterative search, and uses this as the road map for indicator allocation, and then uses semantic similarity and XGBoost algorithm to innovate and construct a system scheme. The test results clearly point out that in the context of the evaluation criteria, the new scheme has been significantly optimized in terms of accuracy and processing speed for a variety of challenges, showing stronger performance superiority. Therefore, in the research of the intelligent evaluation framework of professional English composition, the simulation scheme based on semantic similarity and XGBoost algorithm successfully overcomes the shortcomings of traditional dynamic programming algorithms, and significantly improves the accuracy and operation efficiency of the simulation.