Research on Intelligent Mining Algorithm of English Translation Text Big Data Based on Deep Learning

This paper explores the translation of English texts by a deep learning algorithm, which aims to intelligently extract relevant records from a large corpus of English-translated texts, especially its shortcomings in processing difficult texts and dealing with long-term dependencies. This research integrates the modern long short-term memory (LSTM) network framework, with the aim of improving the accuracy and processing efficiency of the translation. Using a large number of experimental facts, these LSTM models are comprehensively learned and rigorously optimized. The traditional approach is to adapt the style to the language-specific properties of the translated text, and then use a bunch of evaluation metrics to evaluate the overall performance of the model across a large number of text types. The results of this study show that the method significantly speeds up the extraction and processing of information while maintaining the integrity at the translation level. In addition, the study reveals deep learning models’ ability to recognize complex contextual nuances and subtle formulations of language expression. The results of this research are multifaceted, bringing great improvements to device translation structures and facilitating the development of state-of-the-art text content analysis tools. These advances apply primarily to sectors of considerable statistical volume and complexity, including the criminal, scientific, and technological fields. Therefore, this study lays a foundation for future exploration of improving device understanding of language and automation of translation methods in various professional fields.