The automotive open system architecture (AUTOSAR) standard is provided in the form of large-scale textual specifications that are extensive and inherently ambiguous. To verify the compliance of in-vehicle software, consistency testing requires the extraction of configuration information from these documents to ensure conformity with the standard. Based on an in-depth analysis of AUTOSAR specifications and consistency test configurations, this paper proposes an automated method for generating configuration data. First, MinerU is integrated into a graph-based retrieval-augmented generation framework to construct a domain-specific entity-relation knowledge network through semantic enhancement, text refinement and structure-aware chunking strategies. Second, a few-shot chain-of-thought (Few-Shot CoT) prompting technique is employed to guide a large language model (LLM) in extracting test scenarios and the required configuration data from the domain knowledge network. Finally, a configuration item tree with attributes is generated according to configuration rules, and breadth-first search (BFS) and depth-first search (DFS) algorithms are applied to validate and complete the configuration data, resulting in ARXML configuration files compliant with the AUTOSAR standard. Experimental results on 15 AUTOSAR basic software modules, evaluated using 8 metrics and compared with traditional configuration data generation methods, show that the proposed approach achieves an F1 score of 97.51% in implicit dependency extraction, reduces the manual correction rate to 5% and increases the number of generated test scenarios by 3%. The configurations generated by the proposed method not only assist testers in identifying hard-to-locate software anomalies but also significantly improve the completeness of implicit configuration dependency extraction as well as the quality and efficiency of configuration file generation.