In black-box attacks based on substitute training, the similarity of the substitute model to the target model is critical for successful attacks. However, existing schemes merely train the substitute model to mimic the outputs of the target model without fully simulating the decision space, resulting in the adversarial samples generated by the substitute model being classified into the non-target class by the target model. To alleviate this issue, we propose a novel Inter-Class Topology Alignment (ICTA) scheme to more comprehensively simulate the target model by aligning the inter-class positional relationships of two models in the decision space. Specifically, we first design the Position Exploration Sample (PES) to more thoroughly explore the relative positional relationships between classes in the decision space of the target model. Subsequently, we align the inter-class topology between the two models by utilizing the PES to constrain the inter-class relative position of the substitute model in different directions. In this way, the substitute model is more consistent with the target model in the decision space, so that the generated adversarial samples will be more successful in misleading the target model to classify them into the target class. The experimental results demonstrate that our ICTA significantly improves attack success rate in various scenarios compared to existing substitute training methods, particularly performing efficiently in target attacks.