## 1 Introduction  
Existing requirement management primarily relies on document-based formats [1], which, while facilitating communication and understanding, suffer from the inherent ambiguity and vagueness of natural language [2]. This often leads to inconsistencies in interpretation [3], poor information interrelation [4], and increased management complexity [5]. Especially when dealing with large and intricate requirement documents, traditional man ual management approaches are prone to information loss and misinterpretation, adversely affecting both the efficiency and quality of the design process [6]. Furthermore, the lack of an effective traceability mechanism complicates the retrieval of historical change information, exacerbating communication barriers between requirements and design [7]. To address the aforementioned challenges, the Model-Based Systems Engineering (MBSE) approach, particularly the application of the Systems Modeling Language (SysML), provides theoretical support for the standardization and structuring of requirements. SysML defines five major categories of system requirements -- functional requirements, interface requirements, performance requirements, physical requirements, and design constraints [8] -- offering a structured framework that effectively supports requirements management [9], traceability [7], and verification [10]. However, the creation of SysML models still heavily relies on the manual interpretation and processing of natural language requirements by designers, which not only demands substantial time investment, but also requires designers to possess deep domain knowledge [11]. When faced with large volumes of complex requirement documents, maintaining a consistent understanding of the document contents remains a significant challenge, further limiting the application of SysML in efficient requirement management [12]. Additionally, SysML faces limitations in the semantic representation of requirements, making it difficult to effectively capture the implicit relationships between requirements in complex systems, thus hindering its effectiveness in managing complex requirement relationships [13]. To bridge this gap, AI-driven approaches have emerged as promising solutions. Large Language Models (LLMs), leveraging their robust knowledge repositories and exceptional contextual comprehension capabilities, provide efficient and precise technical support for extracting critical information from unstructured requirement documents [14]. Using prompt engineering, designers can construct task-specific prompts to further optimize a model's recognition and extraction efficiency of essential requirement components, significantly enhancing data processing accuracy and consistency [15; 16; 17]. This foundational capability positions LLMs as pivotal enablers for knowledge graph construction. Integrated with graph representation techniques [18], LLMs systematically analyze entity relationships and dependency patterns within requirement texts, transforming unstructured textual information into multi-relational tuples [19]. This transformation facilitates the establishment of structured semantic models for requirement networks in complex systems by using knowledge graph formalization. To address the issues discussed above, this paper proposes a prompt-engineering-based system for constructing requirement representation knowledge graphs, aimed at automatically converting system requirements (SRs) from requirement documents into a visualized knowledge graph structure. To enable the effective processing of requirement documents, this paper focuses on SysML's standardized and structured definitions of requirements, categorizing them into five major types: functional requirements, interface requirements, performance requirements, physical requirements, and design constraints. The corresponding general information extraction templates for each category are designed, which clearly define the key elements of information in the requirement documents, such as subjects, objects, and actions.