Class Incremental Learning (CIL) requires a model to continuously learn new classes without forgetting previously learned ones. While recent studies have significantly alleviated the problem of catastrophic forgetting (CF), more and more research reveals that the order in which classes appear have significant influences on CIL models. Specifically, prioritizing the learning of classes with lower similarity will enhance the model's generalization performance and its ability to mitigate forgetting.Hence, it is imperative to develop an order-robust class incremental learning model that maintains stable performance even when faced with varying levels of class similarity in different orders.In response, we first provide additional theoretical analysis, which reveals that when the similarity among a group of classes is lower, the model demonstrates increased robustness to the class order.Then, we introduce a novel Graph-Driven Dynamic Similarity Grouping (GDDSG) method, which leverages a graph coloring algorithm for class-based similarity grouping. The proposed approach trains independent CIL models for each group of classes, ultimately combining these models to facilitate joint prediction.Experimental results demonstrate that our method effectively addresses the issue of class order sensitivity while achieving optimal performance in both model accuracy and anti-forgetting capability.Our code is included in the supplementary materials.