Class incremental learning (CIL) has attracted a great deal of attention as an effective way to realize lifelong learning. However, existing works still face catastrophic forgetting, i.e., performance degradation on old tasks after learning new category information. In this work, we aim to alleviate this problem through feature space calibration. Specifically, we propose a novel loss function that allows the network to focus more on inter- and intra-class information to extract effective features. The balance between remembering old classes and learning new classes is achieved by learning class relationships rather than just information about a particular class, which can effectively alleviate catastrophic forgetting. Unlike existing methods, the approach proposed in this paper is highly general and flexible and can be directly integrated into existing CIL methods to effectively improve their performance. Our proposed approach is shown to be effective through comparative experiments on three popular datasets: CIFAR100, ImageNet100, and ImageNet1k. To ensure a robust comparison, we utilized three state-of-the-art methods as our baseline models. The results of these experiments demonstrate that our approach outperforms the baseline models on a range of benchmark datasets, showcasing its superiority and potential for broader application.