Extracting protocol interactions from textual specification documents written in natural language is useful, especially when to verify the correctness of a protocol before its implementation and application, or when to generate test cases for protocol-connected systems directly from specification documents. Existing approaches for this purpose rely on deep learning or large language models (LLMs). The deep learning approaches require large-scale and high-quality annotated datasets. They may not work well across protocols in different domains due to limitations imposed by the training datasets, and suffer from difficulties in transfer. The LLM-based approaches offer better generalizability, but existing work only uses simple prompt templates. It does not carefully utilize extraction examples in LLM prompting, and the information extraction process lacks optimization, which affects the effectiveness of the proposed approaches. To address these challenges, this study proposes an enhanced LLM-based method for extracting protocol interactions from protocol texts, based on linguistic expression pattern analysis. Specifically, real-world protocol description texts are first analyzed to summarize common linguistic expression patterns in such texts. Then, representative protocol description examples exhibiting these patterns are selected, and corresponding extraction rules are distilled. Further, these examples and rules are integrated to design a rule retrospection chain-of-thought method for LLM-based protocol interaction extraction. Finally, multi-path inference and self-verification techniques are used to optimize the task execution process. Experimental results on multiple protocol datasets show that the proposed method outperforms the baseline methods in terms of precision and recall of protocol interaction extraction, which confirms the effectiveness of the proposed method.