Policy terms are characterized by timeliness, low frequency, sparsity, and compound phrases. To address the difficulty of traditional term extraction methods in meeting demands, we design and implement a semantic enhanced multi-strategy system of policy term extraction. The system models the features of policy texts from the two dimensions of frequent item mining and semantic similarity. Feature seed words are selected by integrating multiple frequent pattern mining strategies. Low-frequency and sparse policy terms are recalled by pre-training the language model and enhancing semantic matching. Transforming from a cold start without a thesaurus to a hot start with a thesaurus, the system achieves semi-automatic extraction of policy terms. The proposed system can improve the effect of policy text analysis and provide technical support for the construction of a smart government service platform.