[Objective] This paper develops a smart question-answering model for association policy based on cognitive semantic knowledge understanding, aiming to improve the government services. [Methods] First, we established a model based on policy connotation to express policy knowledge. Then, we introduced the attention mechanism for question words and classified policy issues combining the improved ERNIE + CNN model. Third, we used the semantic role labeling IDCNN + CRF model and cognitive computing method to obtain the semantics and pragmatic knowledge. Finally, based on knowledge fusion and semantic retrieval, we utilized knowledge aggregation technology to generate relevant answers. We also adopted the BERT semantic similarity calculation and knowledge unit measurement to evaluate the quality of answers. [Results] The accuracy of problem classification reached 90.76%, which was 18.81% and 5.05% higher than those of the original BERT and ERNIE models. The precision of problem knowledge acquisition reached 95.88%, and the accuracy of the answer quality reached 93.75%. The semantic similarity of the answers was 0.88, while the knowledge consistency was 0.96. [Limitations] The performance of our model is limited by the integrity of the domain knowledge system, while the answers’ relevance relies on the accuracy of policy knowledge extraction. [Conclusions] Based on the deconstruction of policy contents and scientific knowledge representation, the proposed method can generate answers for questions on different policy contents.