Design of Networked Intelligent Translation System Based on Machine Learning Algorithm

Abstract

China’s social and economic development is relatively rapid, and China’s opening to the outside world is further expanding. Under this background, the demand for foreign language professionals is also expanding. This paper designs a networked intelligent translation system based on machine learning algorithm, and proposes a cross-language information extraction method based on bilingual word representation. The learning of bilingual word representation can be divided into two stages: unsupervised and supervised. Capturing Chinese and English bilingual semantic information. The cross-language information extraction method based on machine learning algorithm reduces the impact of translation errors and language gaps on cross-language classification performance, obtains effective bilingual semantic information and annotation information, and improves the cross-language information extraction performance. The test results show that the designed automatic English translation system can effectively and quickly realize intelligent English translation of memory-assisted long characters, with high data recall rate, good accuracy and reliability, and good system compatibility, which is worthy of popularization and practical application.

Artificial intelligence-based design of English teaching system

“…By mobilizing various variables in teaching activities, optimizing learning resources and steps, and using systematic methods, teaching objectives, teaching conditions, teaching techniques, and teaching methods are all derived from learning theory and teaching theory. The author of [23] pointed out that instructional design is a systematic process in which every component is crucial to successful learning. The construction of the instructional design model with the method of instructional design system began in late World War II.…”