Yaodan Liang
School of Foreign Studies, Yulin Normal University, Yulin, 537000, China



The existing translation methods have the problem of imperfect prediction model, which leads to high KSMR value. This paper designs an interactive English Chinese oral machine translation method based on feature extraction algorithm using the management techniques of biotechnology field. The mathematical features of language are extracted, the trestle search method is used for pruning, the human-computer interaction interface is set up, the list of word candidate translation items is loaded, the interactive English Chinese oral machine translation prediction model is constructed, the linear interpolation is used for approximate solution, the feature extraction algorithm is used to design decoding strategy, and the discrimination of feature items for text topics is measured. Experimental results: compared with the other two translation methods, the mean value of KSMR of the designed translation method is 38.87%, 48.18% and 48.51%, which proves that the translation method integrating feature extraction algorithm has higher practical application value.