Research on the Optimization Strategy of English Translation System Based on Fuzzy Control Algorithm and Its Effectiveness in Teaching Application

Abstract

Addressing the current shortcomings of English machine translation, such as poor accuracy and ambiguity, this paper proposes a research approach based on constructing semantic mapping relationships, determining semantic order, and achieving accurate machine translation. After processing and analyzing the context of natural language using generalized relationships between concepts, fuzzy evaluations are conducted on the objectives between different ontology concepts to obtain the set of symbols with the highest semantic relevance within the domain knowledge, thereby establishing the semantic model for machine English translation. The maximum entropy training algorithm is used to classify the semantics of English machine translation, and the weighted hierarchical analysis method is employed to calculate English semantic similarity based on fuzzy selection rules, thereby achieving semantic reordering in English machine translation. Combining the fuzzy decision tree method, a hierarchical English machine translation model is constructed, and optimization strategies for the English translation system are proposed. The designed translation model demonstrates reliable application performance in assisting college English teaching, with course effectiveness achieving a positive satisfaction rate of 70.00% or higher among students.