Topic modeling enables automated identification of latent thematic patterns within unstructured text corpora, facilitating efficient knowledge discovery. However, traditional cluster-based topic models often suffer from poor topic coherence due to the unsupervised nature of clustering algorithms and lack the capability to dynamically adjust clustering granularity. To address these limitations, we propose LLM-Guided Hierarchy-Aware Topic Modeling (LHATM), a hybrid framework that integrates hierarchical clustering with LLM feedback. LHATM begins with density-sensitive hierarchical clustering to build a stable topic tree, then leverages LLMs to refine the hierarchy through dynamic split-and-merge operations. In addition, ambiguous points arising from cluster adjustments are reallocated using a hybrid semantic-geometric strategy, ensuring both robustness and interpretability. Experiments on seven benchmark datasets show that our approach consistently produces more coherent and adaptable topics than existing methods.