With the rapid development of big data and artificial intelligence technologies, graph databases have gradually become core infrastructure for social network analysis, financial risk control, and knowledge graphs due to their advantages in complex relationship modeling and efficient querying. Graph databases manage two primary objects: nodes and the relationships between them. At the architectural level, the node identifier (NodeID) serves as the critical link for graph data management, undertaking key functions including node identity representation, relationship lookup, and graph algorithm execution. Current mainstream graph databases widely adopt key-value store (KVS) to implement NodeID-to-graph-structure mapping management. However, existing systems largely rely on general-purpose KVSs (e.g., RocksDB) for managing such mappings, yet lack in-depth consideration of workload characteristics. These limitations are reflected in two aspects: (1) the lack of workload modeling for node identifier mapping, and (2) insufficient adaptability to heterogeneous software and hardware environments (e.g., CPU/memory, SSD/HDD). This study first systematically analyzes the operational characteristics and KVS requirements of node identifier mapping in graph databases. It then evaluates the performance of multiple mainstream KVS engines (including RocksDB, LMDB, LevelDB, FasterKV, and ForestDB) in heterogeneous hardware environments, systematically revealing the impact patterns of data workloads (e.g., data scale, read-write ratio) and hardware configurations (e.g., memory capacity, thread count, storage medium) on execution efficiency. Based on large-scale experiments involving five datasets, three hardware platforms, and over 1 300 comparative tests, this study proposes an adaptation strategy based on decision tree model that integrates workload characteristics (data scale and read-write ratio) with hardware configurations (e.g., memory, thread count, disk type) to guide adaptive selection of KVS engines. Experiments show that the model achieves 92.1% accuracy in recommending optimal engines, with suboptimal scenarios exhibiting less than 10% performance gap.