With the development of ESL education in recent years, real-time language translation has gradually become a research hotspot in the field of natural language communication. In this paper, the data processing technology of real-time translation is introduced, and then a Transducer (RNN-T) model based on real-time language translation is constructed based on the BPE subword segmentation method under the condition of probability gradient calculation algorithm and transcription network. Secondly, this model determines and tests the content involved in the process of real-time language translation and finally obtains results to improve the quality of real-time language translation. The experimental results show that the accuracy of character translation among the six language models is 85.8%, 86.2%, 88%, 93.4%, 94.03%, and 94.36%, respectively. In addition, the error rate of inserting, deleting, and replacing characters is < 20%. Compared with the other four models, the word error rate of the RNN-Transducer algorithm is reduced by 1.59%, 3.94%, 3.86%, and 9.01%, and the RTF is reduced by 3.91%, 10.96%, 10.09%, and 7.94%, respectively, which reflects the superiority of the RNN-Transducer method proposed in this paper.