关键词: 麻醉护理；政策；文本挖掘

Abstract: Objective: To conduct a quantitative analysis of national-level policy documents related to anesthetic nursing using text mining methods, providing references for policy formulation and optimization. Methods: Fifteen policy documents were analyzed with ROSTCM and SPSSAU, employing high-frequency word statistics, semantic network analysis, and Latent Dirichlet Allocation (LDA) topic modeling. Results: China's anesthesia care policies are primarily issued by the National Health Commission. Semantic network analysis revealed four core layers: standardization of anesthesia care services, professionalization of talents, extension and continuity of services, facility guarantee and safety management. LDA topic analysis identified three main themes: perioperative nursing quality management and human resource optimization, peri-anesthesia management and monitoring, and service optimization under the concept of enhanced recovery. Conclusion: China's anesthesia nursing policies are currently in a phase of continuous improvement. In the future, efforts should be made to strengthen interdepartmental collaboration, optimize the policy framework, refine measures, establish quality control and feedback mechanisms, and promote the sustainable development of the anesthesia nursing profession.

Key words: anesthesia nursing; policy; text mining