The core of translation is language conversion, but it should be more of an aesthetic experience, a transformation of different ways of thinking. This paper proposes a neural semantic analysis method for English translation based on text similarity calculation. The technique uses a large-scale corpus to train the vector space model of words, establishes a concept-context matrix through the term-document matrix, and determines each value of the concept vector with a weight according to the literary translation in the knowledge base. The information extraction and evaluation of the English-Translated text through semantic similarity calculation provides a theoretical basis for the subsequent analysis of style conversion and linguistic, and aesthetic reproduction of the English-Translated text. The study will take the Lin Yutang translation and the Ricardian translation of The Analects of Confucius as an example to develop the analysis of style transformation and reproduction of linguistic beauty of the English-Translated text from both lexical and syntactic levels. The results show that the lexical density of Lin Yutang's translation is 0.5866, while that of Rie Jacob's translation is 0.5334, with the former significantly larger than the latter. It shows that Lin Yutang's translation of The Analects of Confucius adopts a naturalization strategy, i.e., To disseminate Chinese culture in a way that is authentic, familiar to English readers, and easy to understand, so as to make his linguistic expression more emotional. © 2024 Han Gong, published by Sciendo.
