An Interpretation of Cross-cultural English Translation Teaching Strategies under Clustering Algorithm

Abstract

Intercultural English translation ability is an essential requirement for college students, and this paper uses a clustering algorithm to study the current status of teaching intercultural English translation. This paper first proposes the basic process and application scenarios of clustering algorithm, and explains the rationality of using clustering algorithm in the analysis of this paper. Then the improved elbow method is used to automatically determine the number of clusters to accurately obtain the number of classifications of user data, and the data with small lof values are screened as the candidate set of initial clustering centroids, and the outlier lof algorithm is used to weight the distance between data in data set F to iteratively optimize the clustering centroids. Finally, the analysis was conducted to explore the problems in terms of students' learning motivation, translation influence factors and teaching evaluation. In terms of teaching effectiveness, 90% believe that the current English translation teaching is mainly exam-oriented and plays little role in professional competence development. In terms of teaching strategy improvement, 90% of them think that English practice teaching should be strengthened, with more translation activities, projects, and translation classes. This is 1%, 2%, 10%, and 1% more than innovative teaching methods, strengthening cultural penetration, adopting diverse translation techniques, assessing learning effects in a reasonable and timely manner, and result-oriented teaching strategies, respectively. The English translation teaching classroom should be student-oriented and innovative teaching strategies should be used to optimize learning outcomes.