[Objective] This paper proposes a new method to analyze policy text relevance, aiming to retrieve more in-depth semantic information. [Methods] First, we built a new algorithm combining the dependency parsing analysis and word embedding model. Then, we analyzed the semantic relevance of policy texts from the perspective of sentence and word meaning information. Our method fully utilized the language characteristics of the policy texts to establish the extraction rules for dependency syntax. [Results] For test dataset with a relatively low degree of policy text association, our new algorithm’s F1 value reached 0.857, which was 22.78% higher than the algorithm fusing TF-IDF and cosine similarity. We also described policy text relevance with the subtle word differences. [Limitations] For semantic inforamiton mining, more research is needed to train word vector models for specific policy domains to further improve their accuracy. In sentence information mining, the accuracy of existing dependency syntactic analysis tools could be improved. [Conclusions] The proposed algorithm could effectively reveal the policy text association, as well as bring new research perspectives and tools for quantitative research on policy texts.