The robustness of a network reflects its ability to maintain functionality in the face of attacks or failures. However, current methods for calculating robustness metrics rely on simulating attacks, which is computationally complex and severely time-consuming, resulting in poor scalability. To address this problem, this paper proposes a topology robustness prediction method based on hierarchical feature fusion and progressive learning, which utilizes the multi-scale features of the network structure to achieve an efficient and accurate evaluation of robustness. Specifically, we first design a node multi-scale feature extraction module to comprehensively capture the multi-level structural characteristics of the network topology; second, we propose a progressive input strategy, which enables the model to focus on different levels of structural information in phases by progressively introducing features of different scales, thus improving the prediction accuracy of the model. Finally, the model is extensively evaluated by testing it on several different types of networks as well as real-world networks. Experimental results show that we significantly improve the robustness computational efficiency compared to the simulated attack method, while outperforming other methods in robustness prediction performance.