In recent years, with the rapid development of LLMs, agent and RAG technologies, multi-round human-computer interaction applications increasingly require efficient retrieval of dialogue content to understand user intent and generate coherent responses. However, existing methods mostly rely on fixed windows or single-round dialogue embeddings, which struggle to dynamically adapt to context changes, suffering from issues such as information truncation, information noise, and no-explicit semantic associations. To address these challenges, this paper proposes a dynamic context-aware embedding method, which leverages LLMs to infer the relevant historical range associated with the current utterance, filters highly related dialogue content through semantic matching, and generates natural language instructions to explicitly model cross-round semantic chains. Experiments demonstrate that this method significantly outperforms traditional embedding approaches on the LoCoMo dataset and exhibits strong robustness across various dialogue scenarios. This work provides new ideas and practical approaches for building intelligent agent systems with semantic understanding and dynamic memory capabilities.