Algorithm Design and Application of Grammatical Structure and Function Learning in Chinese Writing for Intelligent Analysis

This study utilizes the Larsen-Freeman three-dimensional theory of grammar teaching to quantitatively analyze the grammatical attributes “form”, “meaning” and “usage” in the Chinese writing process. The study uses the PCFG model to extract the syntactic dependency tree and represents the features of the tree structure with the help of TF-IDF. Meanwhile, the Transformer model extracts the semantic and syntactic features of sentences. At the same time, the positional encoder is removed to ensure that the model obtains information from the syntactic level. Further, an unsupervised method for generating grammatical textual recapitulation for Chinese Writing is proposed, as well as a Grammar Tree Generator for generating recapitulated sentences, which efficiently extracts the features of the input grammatical sequences. In addition, the study also includes writing feature analysis based on grammatical function matching, using a hierarchical clustering algorithm to analyze the similarity of 60 grammatical functions. Finally, validation was performed on 30 Chinese writing text collections, each containing 10 compositions, and the results showed high accuracy of unlabeled grammatical function recognition. The LDA model determined the optimal number of writing topics to be 150. This study highlights the potential application of intelligent analysis techniques in improving the quality of Chinese Writing. It provides new perspectives for an in-depth understanding of the interplay between grammatical structures and functions in the Chinese writing.