Research on Automatic Evaluation System of English Writing Teaching Software Based on Natural Language Processing

English writing is an important symbol to measure learners' comprehensive language use ability. The traditional manual marking method has some disadvantages, such as time-consuming, laborious, untimely feedback and difficult to be comprehensive. With the rapid development of natural language processing (NLP) technology, its application in the field of education gradually shows great potential. This paper aims to develop an automatic marking system for English writing based on NLP, so as to improve the efficiency of writing teaching and students' writing ability. The system adopts hierarchical architecture, including data layer, processing layer, service layer and application layer to ensure the scalability and maintainability of the system. The main functional modules include composition management, automatic grading, error detection and correction, writing style analysis and feedback and report. The system trains a large number of English composition samples through the deep learning model, realizes automatic grading and error detection, and provides detailed feedback reports, including corrections of grammatical errors, spelling errors and improper vocabulary use. The experimental results show that the system is excellent in scoring accuracy, and the correlation coefficient with expert scoring is as high as 0.9 or more. The system can not only give the total score, but also provide personalized feedback reports to help students understand their own shortcomings more clearly and make improvements. Compared with traditional marking methods, automatic marking system has obvious advantages in marking efficiency, scoring consistency and personalized feedback ability. The research in this paper provides new ideas and methods for the automation and intelligence of English writing teaching, which has important theoretical significance and practical value.