The issue of hallucination detection and correction in LLMs is receiving increasing attention. Existing methods primarily pursue fine-grained hallucination detection through various fact unit decomposition techniques; however, we observe the following limitations in these approaches: (1) excessive redundant judgments lead to low detection efficiency; (2) cross-unit semantic dependencies such as coreference, temporal consistency, and event relations are ignored, yielding context-agnostic, unit-local decisions. Inspired by syntactic structures in linguistics and syntactic parsing work in natural language processing, we propose a novel two-stage hallucination detection paradigm for LLMs called “syntactic decomposition-hallucination detection”, along with a complementary method for hallucination detection and correction, termed SCD-HDC. In the hallucination detection phase, SCD-HDC incorporates a syntactic decomposition step to refine the detection granularity to syntactic component quadruples, which maintains the semantic integrity both within and between fact units while avoiding redundant detections. In the hallucination correction phase, the method utilizes the hallucinated syntactic component labels from the detection results as a guide, achieving flexible multi-scale corrections with high precision. Experimental results on three datasets from RAGTruth indicate that, in the hallucination detection phase, SCD-HDC achieves an overall F1 score that is more than 10% higher for the response level and over 24% higher for the span level compared to the best baseline methods. In the hallucination correction phase, it reduces the correction scope by an average of 65.4% compared to baseline methods, while still obtaining the best correction accuracy on two datasets. Furthermore, experiments demonstrate that SCD-HDC has good adaptability to multiple models.