Research on the Optimal Selection Method of Fuzzy Semantics in English Long Sentence Machine Translation

Abstract

In order to improve the accuracy of English long sentence machine translation, the fuzzy semantic optimal selection model of English long sentence machine translation is constructed by combining fuzzy semantic optimal selection and feature extraction methods. The fuzzy semantic optimal selection model of English long sentence machine translation based on adaptive learning of machine neural network is proposed, and the constraint object model of fuzzy semantic selection of English long sentence machine translation is constructed. The method of context correlation mapping is used to analyze the fuzzy semantic features and construct the ontology structure model in the process of English long sentence machine translation. The linear mapping and statistical information analysis of English long sentence machine translation are realized by using the linear semantic ontology structure mapping mechanism and the corresponding text sequence parameter mapping in the dictionary, and the language semantic correlation calculation model of the optimal selection of fuzzy semantics in English long sentence machine translation is established. The machine neural network adaptive learning method is adopted to realize the segmented learning control of the non-sentence backbone in the process of fuzzy semantic selection of English long sentence machine translation. Weighted learning and adaptive weight analysis are realized according to the machine neural network adaptive learning result of the optimal selection of fuzzy semantic of English long sentence machine translation, and the optimal design of fuzzy semantic optimal selection model of English long sentence machine translation is realized. The simulation results show that this method is robust and the evaluation result is accurate, which improves the accuracy and anti-interference of English long sentence machine translation.