A Study on Source Analysis and Correction of Chinese-English Poetry Translation Errors Based on Data Mining

Abstract

When the traditional poetry translation model can not be applied to the translation requirements of poetry context, it is necessary to improve the backward translation model. To address the issues in the traditional translation model, this paper utilizes the error in the translation model of the improved clustering algorithm for correction. The poetry translation model’s overall framework is explained in detail, and each module code is analyzed. After optimizing the data in the model, the reasons for the model translation error are analyzed and corrected to achieve a perfect fit between the Chinese and English translations of the poems. The results of the study show that the errors in poetry translation are mainly caused by words and sentences, as analyzed in this paper. This paper also corrects the clustering algorithm for related errors and proposes a model for correcting translation errors in the Logistics Chaos Model. Finally, it is concluded that words and sentences are the key factors that affect the English translation of poetry. Compared with SDPS, LDifC, WFBC, LDivC, and FC, its correctness rate reaches more than 96%, 93%, 92%, 92%, and 95% after correction, respectively. Compared with the pre-correction, their accuracy increased by 0-3.06%, 3.06%-21.05%, 2.11%-10.2%, 4.35%-9.68%, and 0-8.25%, respectively. It can be seen that the translation model with an improved clustering algorithm proposed in this paper is of great significance for the improvement of the accuracy of the DWMA translation text model for an English translation of poetry.