Research on the Automatic Scoring Method of English Translation System by the Clustering Algorithm

The role of the clustering algorithm in the automatic scoring of the English translation system is very important, but there is a problem of poor scoring accuracy. Simple English scoring methods do not solve the correlation problem of sentence translations in automatic scoring, and there is less correlation. Therefore, this paper proposes a clustering algorithm to construct an automatic scoring model. Firstly, the semantic knowledge is used to classify the English translation content, and the English content is divided according to the scoring criteria to realize the standardized processing of sentences. Semantic knowledge then classifies sentences into English translation collections and iteratively analyzes the scored content. MATLAB simulation shows that under the condition of a certain number of words, the clustering algorithm's scoring accuracy and translation time are better than the simple English scoring method.