To automatically grade compositions, automatic composition grading primarily employs statistics, mathematical analysis, machine learning, natural language processing, and other technologies. This paper presents a machine learning-based intelligent evaluation model for English composition. As a result, this paper proposes a word vector grouping-based text content representation method and a vector space model-based text content representation method. The Word2Vec model is first trained with words, then used to generate a test model of word vectors, with the statistical information of the corresponding words in each category serving as content text features. The results show that when features based on word vector clustering are added to the content text, the effect of each model improves significantly, especially the maximum entropy model, which improves by 0.048. The XGBoost model has also seen a significant improvement, going from 0.771 to 0.803. The test corpus is made up of 100 articles chosen at random from the corpus, and the test set is checked for errors. The rate of accuracy is 68 percent. The conclusion demonstrates that the model presented in this paper can help promote English teaching reform and quality-oriented higher education while also easing the burden on teachers and students.