Design and research of English material archives management system based on machine learning technology

With the background of globalization, the efficient management and intelligent retrieval of English materials and archives have become critical requirements in academic, educational, and commercial fields. Traditional archives management systems depend on manual classification and retrieval, which could be more efficient if Aniston copes with massive amounts of data. This study aims to explore the optimal design of an English data archives management system based on machine learning technology to improve the accuracy and efficiency of data retrieval. By introducing deep learning models neural network (CNN) and recurrent neural network (RNN), we construct a document classification, keyword extraction, and topic recognition system, which significantly improves the accuracy of data retrieval. After training and testing on a large-scale English corpus, the classification accuracy of the model has increased from 75% of the traditional method to 90%, and the F1 score of keyword extraction has increased from 60% to 85%, demonstrating the strong application potential of machine learning in English data management.