Research on Artificial Intelligence Machine Translation Based on Fuzzy Algorithm

In the technical field, achieving semantics for AI machine translation is a challenge that must be overcome. The model uses rule reasoning in natural language processing for deep understanding, and combines machine learning theory to optimize model parameters, so as to achieve the purpose of improving translation accuracy. After experimental verification, the model proposed in this paper shows a high accuracy rate of more than 90% when translating different translations; in addition, compared with DocNMT and SENTNMT, two common artificial intelligence machine translation models, the model proposed in this paper shows higher accuracy in machine translation, up to 97%, far exceeding them. At the same time, the model also has a certain degree of fault tolerance, which can ensure the stability and reliability of the machine translation system. Therefore, using the method proposed in this paper, the quality level of machine translation can be significantly improved.