As generative artificial intelligence and natural language processing continue to enter the language teaching scene, how to improve the semantic depth, task adaptability and teaching usability of English writing feedback in higher vocational colleges has become a practical issue in English teaching reform. Focusing on the needs of English writing training in higher vocational colleges, this paper constructs an AI writing feedback mechanism based on deep semantic understanding. Relying on corpus preprocessing, contextual semantic representation, task context matching, error detection, discourse coherence evaluation and multi-task collaborative optimization, this paper realizes a closed-loop design from text input, problem diagnosis, feedback generation and teaching application. The experimental results show that the average precision, recall and F1 value of the model on the error recognition task reach 92.4%, 90.5% and 91.4%, respectively. In terms of feedback content semantic matching, task alignment and suggestion relevance, the proposed method achieves 89.6, 87.9 and 90.8, respectively. In the teaching application, the total score of the post-test in the experimental group increased from 70.5 to 81.7, and the acceptance rate of students ‘feedback increased from 68.4% to 84.6%. The research shows that this method can effectively improve the feedback quality and revision efficiency, and has practical significance for promoting the intelligent and refined development of English writing teaching in higher vocational colleges.
