Design of English Machine Translation System Based on Ant Colony Algorithm

Conventional English machine translation systems mainly use Finetune transfer learning to obtain language feature parameters, which is vulnerable to changes in the weight of RNN translation coding, resulting in low BLEU translation evaluation indicators. Therefore, a new English machine translation system is designed based on ant colony algorithm. The EMMY simulator and FXI32 static translator are designed, and the English machine translation features are extracted, and an English translation model is constructed using ant colony algorithm, so as to achieve effective translation. The system test results show that the designed English machine translation system based on ant colony algorithm has a high BLEU translation evaluation index, which demonstrating the excellent performance, reliability, and practical value of the designed English machine translation system, and it has made a considerable contribution toward reducing translation costs and improving translation quality.