Modeling embedded systems is an essential component of model-based software development. The architecture analysis and design language (AADL), with its ability to formally express hardware-software structures and interaction relationships, is widely applied in system design. Large language models (LLMs) provide a new pathway for generating architecture models from natural language requirements. However, existing approaches exhibit significant limitations in requirement semantic understanding, boundary identification of AADL components, and construction of connection relationships, which constrain their practicality and the quality of generated models. To address these challenges, this study proposes an intelligent modeling approach for embedded systems, termed SmartGen-AADL. The overall framework is built upon a multi-agent collaboration mechanism, integrating key techniques such as semantic parsing, structural recognition, and prompt-enhanced generation, thus enabling high-quality transformation from natural language requirements into structured AADL models. The method consists of three core stages: (1) a structural agent identifies system architectures from system architecture documents and extracts standardized requirement statements; (2) a sub-problem agent performs item-level analysis and interaction mining to refine requirement granularity and explicitly model component interactions; (3) a component generation agent incorporates structural guidance and retrieval-augmented generation (RAG) of similar components into semantic prompts, guiding the LLM to produce component code that conforms to AADL syntax. To support this process, a knowledge base of “itemized requirements-AADL components” and a semantic alignment dataset of “system architecture documents-AADL architectures” are constructed. Experimental results on 15 embedded system application scenarios demonstrate that, compared with approaches solely relying on prompt engineering, the proposed multi-agent collaborative modeling method achieves significant improvements across four mainstream LLMs. Among them, the performance gains are most pronounced on the DeepSeek-r1 model: the component code error rate is reduced by an average of 34.37%, F_BERT semantic similarity is increased by 6.21%, structural matching accuracy improves by more than 20%, and human evaluation scores rise by approximately 0.7 points. Furthermore, results from the ablation study reveal that the sub-problem identification mechanism enhances control over modeling granularity. The system structure tree contributes to component organization and hierarchical topology information. The retrieval-augmented generation mechanism supplies external knowledge support and reduces hallucination. Communication connection recognition ensures interface completeness and closed interaction loops. The synergy of these four mechanisms substantially promotes alignment between natural language requirements and the AADL modeling language, thereby improving model consistency.