Many modern software systems provide numerous configuration options with a large parameter space that users can adjust for specific running environments. However, configuring such systems always incurs an undue burden on users due to the lack of domain knowledge to understand complex interactions between the performance and the parameters. To address this issue, various tuning techniques have been developed to automatically determine the optimal configuration by either directly searching the configuration space or learning a surrogate model to guide the exploration process. Most previous studies only apply simple search strategies to explore the complex configuration space , which often leads to fruitless attempts in suboptimal areas. Inspired by previous studies, we define configuration structures to describe the positions of various configurations in the performance space of software systems. This idea leads to the design of a novel Configuration Structure-Aware Tuning (CSAT) algorithm. CSAT constructs a structure model for system configurations using the framework of Adaptive Network-based Fuzzy Inference System (ANFIS), learns a comparison-based distribution model through Gaussian Process Regression (GPR), and uses Bayesian Inference to generate potentially promising configurations based on the structure. The experimental results demonstrate that in terms of tuning performance, on average, CSAT outperforms default configurations by 65.51% and outperforms six state-of-the-art tuning algorithms by 22.10%–33.20%. In terms of handling internal constraints, CSAT achieves an average probability of 0.767 in generating valid configurations.