From a pragmatic perspective, this study systematically evaluates the differences in performance among representative large language models (LLMs) in recognizing politeness, impoliteness, and mock politeness phenomena in Chinese. Addressing the existing gaps in pragmatic comprehension, the research adopts the frameworks of Rapport Management Theory and the Model of Mock Politeness to construct a three-category dataset combining authentic and simulated Chinese discourse. Six representative models, including GPT-5.1 and DeepSeek, were selected as test subjects and evaluated under four prompting conditions: zero-shot, few-shot, knowledge-enhanced, and hybrid strategies. This study serves as a meaningful attempt within the paradigm of ``Great Linguistics,'' offering a novel approach to applying pragmatic theory in the age of technological transformation. It also responds to the contemporary question of how technology and the humanities may coexist, representing an interdisciplinary endeavor that bridges linguistic technology and humanistic reflection.

翻译：本研究从语用学视角，系统评估代表性大型语言模型在识别中文礼貌、非礼貌及虚假礼貌现象时的性能差异。针对现有语用理解研究的空白，本研究采用关系管理理论与虚假礼貌模型框架，构建了融合真实语料与模拟话语的三分类数据集。选取包括GPT-5.1与DeepSeek在内的六种代表性模型作为测试对象，在零样本、少样本、知识增强及混合策略四种提示条件下进行评估。本研究是"大语言学"范式下的一次有益尝试，为技术变革时代的语用理论应用提供了新路径，同时回应了技术与人文学科如何共存的当代命题，体现了连接语言技术与人文反思的跨学科探索。