The rapid globalization trends have significantly elevated the demand for automated English grammar correction technologies. Our research centers on enhancing deep learning-based grammar correction models through Transformer architectures. A novel error correction framework is proposed, leveraging Transformer’s encoder mechanism to contextualize target words, demonstrating effective performance in addressing article misuse, preposition errors, and noun number discrepancies. Comparative analyses were conducted across various sequence-to-sequence architectures to assess their error correction capabilities, while exploring diverse data augmentation strategies for model enhancement. Persistent challenges include limited interpretability of deep classification models, dependency on training corpus scale, scarcity of expert-annotated datasets, and suboptimal recall metrics. Future investigations will focus on developing innovative methodologies to overcome these limitations and drive substantial advancements in the domain of automated grammatical error correction.