Deep learning and fuzzy algorithm in improving the effectiveness of college English translation teaching

With the development of globalization, college English translation teaching is faced with the challenge of dealing with complex language structure and cross-cultural content. The traditional teaching methods are inadequate in evaluating translation quality and correcting translation errors, which is difficult to meet the actual needs of students. This study combines deep learning and fuzzy algorithm to improve the effect of translation teaching. Based on the data analysis of 387 students, the BiLSTM model is used to train translation tasks, and the fuzzy inference system is used to evaluate translation quality comprehensively. The results show that this method improves students’ translation accuracy, fluency and cultural understanding, and reduces common translation errors. The research proves that the application of intelligent technology in translation teaching is effective and provides strong support for the optimization of teaching strategies.