Abstract: We have investigated methods utilizing hierarchical structure information representation in the semantic parsing task and have devised a method that reinforces the semantic awareness of a pre-trained language model via a two-step fine-tuning mechanism: hierarchical structure information strengthening and a final specific task. The model used is better than existing ones at learning the contextual representations of utterances embedded within its hierarchical semantic structure and thereby improves system performance. In addition, we created a mechanism using inductive grammar to dynamically prune the unpromising directions in the semantic structure parsing process. Finally, through experiments{Our code will be published when this paper is accepted.} on the TOP and TOPv2 (low-resource setting) datasets, we achieved state-of-the-art (SOTA) performance, confirming the effectiveness of our proposed model.