Research on the Application of NLP Algorithm Based on Multi-interaction Feature Fusion in English Writing Teaching

The research develops a system integrating Entity Framework, UI processing technologies, and access control models with a focus on natural language processing (NLP) for clause analysis, text segmentation, part-of-speech tagging, phrase cutting, and grammar checking. It examines system architecture, user demographics, functional requirements, and builds a user-centric model. Core technologies covered include database structuring and dynamic/static modeling, with practical examples of how the system supports intelligent English writing training for college students. The study explores a method for integrating diverse behavioral characteristics in an information network to better predict user behavior by combining heterogeneous and homogeneous relationships. In a 16-week study with 300 high school students, one group received traditional training, while the other used an NLP-based multi-interaction feature fusion algorithm. Key interaction features analyzed include grammatical accuracy, lexical diversity, and semantic similarity, alongside short-term interests and temporal information. By using self-attention mechanisms to synthesize temporal and object information, the approach enhances recommendation performance, computational efficiency, and robustness, effectively supporting dynamic user behavior analysis within the training system.