Analysis of Combining Fuzzy Algorithms with Improved Attention Mechanisms in AI-based Machine Translation

In the era of big data, the classification of English teaching texts makes it easy to generalize and analyze massive data. In this paper, an improved mask-BERT enhanced modular neural machine translation model is proposed. Through the internal fusion and dynamic weighting of mask matrix strategy and multi-attention mechanism, English-Chinese and Chinese-English translation tasks are compared on the experimental specific data set and the Python programming crawler network part of the open source English teaching translation website. Compared with the Transformer baseline model, the performance improvement is significant. The research of pre-trained language model for English teaching plays a certain role in promoting the development of natural language processing in English teaching, and has important theoretical significance and wide application value.