Children’s narrative ability is an important indicator of language development and is commonly used in clinical diagnosis and linguistic research. However, the lack of large-scale, standardized, and accurately annotated Chinese child language corpora makes grammatical analysis both time-consuming and prone to subjectivity, while existing automated tools fall short of clinical and research needs. This study introduces MINAS (Mandarin Intelligent Narrative Assessment of Syntax for Children), which integrates the MAIN story framework with the MAPS-R syntactic framework to construct a Chinese narrative corpus encompassing four categories and 20 indicators. We evaluated commercial models (ChatGPT-4, Claude Sonnet 4, Gemini 2.5 Flash, DeepSeek) through prompt engineering, and fine-tuned open-source models (Chinese RoBERTa, OpenHermes-2.5) with LoRA. Experimental results show that few-shot prompting achieves high accuracy across most indicators, while fine-tuning with LoRA achieves better performance in noun and verb phrase identification but is not as good for complex sentence structures. This study validates the feasibility of applying large language models to syntactic classification of Chinese child narrative corpora, highlighting their potential in clinical applications and linguistic research.