Intelligent Recognition System for English-Chinese Translation Based on Ant Colony Optimization Algorithm

Interactive translation is an important auxiliary tool in English - Chinese machine translation. The translation object is mainly word segmentation, which is connected together to form a final translation result. However, in the traditional translation system, the optimal solution can not be obtained because of the lack of accurate selection of semantic features, or the translated text does not conform to the original meaning. Although there are related researches on translation system program development and architecture optimization in China, the research direction is not uniform, and no rich results have been obtained, nor has a perfect one been formed. The ant colony algorithm, as a kind of bionic optimization algorithm, has the characteristics of distributed computing, easy to combine with other bionic optimization algorithms, robustness, positive feedback and global search, especially the superiority of solving complex optimization problems, which is getting more and more attention. Therefore, this paper designs an intelligent recognition system for English-Chinese translation based on ant colony optimization algorithm and combines neural networks for speech recognition functions. The results indicate that this designed system possesses a high recognition rate, shorter recognition time, and enhanced effectiveness. Experimental outcomes demonstrate that the system achieved a 95.7% accuracy rate on the dataset, surpassing three other methods.