Fuzzy logic-supported automatic error recognition and efficient optimisation in literary writing texts

In view of the defects of the existing writing assistance software in identifying errors such as literary texts, to address the deficiencies of existing writing assistance software and improvement model based on fuzzy logic and cognitive load theory.This model constructs a computable cognitive writing load quantification framework and designs a multi-layer fuzzy reasoning system to handle the uncertainty of errors, and then adopts a dynamic programming strategy to generate efficient optimisation suggestions.Experiments based on the public dataset Building Educational Applications 2019 show that this method achieves a score of 62.3% on F0.5, significantly outperforming frontier baseline models such as grammatical error correction: tag not rewrite and text-to-text transfer transformer (p < 0.01), especially showing obvious advantages in dealing with inconsistent styles and logical coherence errors, verifying its effectiveness and advancement.