Abstract: With globalization and technological progress, the demand for language translation is increasing. Especially in the fields of education and research, accurate and efficient translation is considered essential. However, most existing translation models still have many limitations, such as inadequacies in dealing with cultural and contextual differences. This study aims to solve this problem by combining big data analysis, machine learning and translation theory, and proposes a comprehensive translation quality evaluation model. On the basis of screening and constructing a representative sample database, pre-processing and standardization, feature selection is carried out by combining multi-dimensional features such as grammatical complexity and cultural adaptability factors, and different machine learning algorithms are used for model construction and parameter optimization. Finally, by training and testing the model, the performance and effectiveness of the model are evaluated, and a comprehensive evaluation standard is constructed. The results show that this model can not only effectively improve the translation quality, but also has a high system application and universality.
Keywords: Translation quality evaluation, big data analysis, machine learning, cultural adaptability, syntactic complexity
