大语言模型具备自然语言理解、文本生成、语义检索和任务推理等能力，为智慧警务建设提供了新的技术支撑。其可应用于警情摘要、文书辅助、知识问答、反诈预警和线索归并等场景，提升警务运行效率，但也可能带来事实幻觉、数据安全、算法黑箱、责任不清和技术依赖等风险。对此，应从私有化部署、数据脱敏、警务知识库建设、人工复核、权限分级、日志留痕和模型评测等方面完善治理机制，推动大语言模型在智慧警务中的规范化应用。

Large language models provide new technical support for smart policing with their capabilities in natural language understanding, text generation, semantic retrieval and task reasoning. They can improve policing efficiency in alarm summarization, document assistance, police knowledge question answering, anti-fraud warning and clue integration. However, their application may also cause risks such as hallucinated outputs, data security vulnerabilities, algorithmic opacity, unclear responsibility and technological dependence. To ensure standardized application, governance mechanisms should be improved in private deployment, data desensitization, police knowledge base construction, human review, hierarchical authorization, log retention and model evaluation.