Research on English Grammar Error Correction Algorithm Based on Deep Bidirectional Attention Long Short-Term Memory Network

English sentences often contain grammatical errors due to mismatches between words or phrases and the syntax of adjacent sentences. To address this, we propose an English grammar correction algorithm using a Deep Bidirectional Attention Long Short-Term Memory (LSTM) network. This algorithm leverages the bidirectional LSTM's forward and backward information processing and the attention mechanism's dynamic weight allocation to better identify and correct complex grammatical errors through a multi-layer network structure. During data processing, large-scale English text data is preprocessed and annotated to create a training dataset of grammatical errors and their corrections. The designed Deep Bidirectional Attention LSTM model effectively captures long-term dependencies between words and phrases in sentences using deep learning of sequence data. Experimental results show significant performance improvements with the proposed algorithm on multiple public English grammar correction datasets, with accuracy and recall rates increasing by 15% and 12% respectively compared to traditional models. Furthermore, we compare the effects of different model structures and attention mechanisms on correction performance, confirming the superiority of the Deep Bidirectional Attention LSTM. The proposed method uses deep learning technology to significantly enhance English grammar correction accuracy, improve writing assistance tools, and elevate users' grammar proficiency and writing quality.