Multi-sequence cardiac magnetic resonance (MS-CMR) images are capable of providing myocardial pathology information for patients with myocardial infarction. Precise myocardial structure and pathology segmentation hold significant importance for subsequent diagnosis and treatment. Nevertheless, traditional manual myocardial structure and pathology segmentation is not only time-consuming and labor-intensive but also has a low accuracy rate, particularly when identifying pathology such as scars and edema that are small in volume and have low contrast with the surrounding tissues, it becomes even more challenging. To address this issue, this paper proposes an improved nn-UNet for fully automatic segmentation of myocardial pathologies. In this network, based on nn-Unet, we use multi-modal data as input to make up for the lack of information in a single mode. For multi-modal data, we utilize cross normalization to improve the generalization performance. Meanwhile, multi-scale attention modules are integrated to process features at different resolutions, thus improving the feature representation capability of neural networks. Through feature fusion and attention weighting, the model can better capture the global and local information of myocardial pathologies, and achieve more accurate segmentation of myocardial pathologies. To verify the effectiveness of the proposed method, we conducted an evaluation using five-fold cross-validation in the dataset of the MyoPS++ challenge.