Precise structural segmentation of the heart is important for the adjunctive diagnosis of cardiovascular disease and accurate preoperative evaluation. There are significant differences between images of different modalities in terms of spatial distribution and semantic expression， but existing methods mostly use single-branch network structures， which are unable to fully integrate multi-modal information and lack generalization capabilities in multi-modal tasks. To address this problem， this paper proposes a multi-branch collaborative segmentation network， i.e. multi-modal collaborative network （MCNet）， which fuses the state space model Mamba with the convolutional model. The network is mainly composed of three modules： A dual-branch feature extractor based on Mamba and convolutional neural networks， a dynamic feature fusion module， and a Mamba decoder. The dual branches of the feature extractor focus on extracting global semantic and local detail features， respectively， and the dynamic feature fusion module dynamically adjusts the weights of multiple fusion paths according to the image， thus realizing dynamic feature integration in different branches. The proposed method is fully experimented on the MRI dataset ACDC of the heart and the ultrasound dataset CAMUS. Experimental results show that the proposed method， through a dynamic feature fusion module based on the mixture of experts （MoE） mechanism， dynamically adjusts the fusion weights of Mamba global features and CNN local features. In the ACDC dataset with clear boundaries， the average Dice and intersection over union （IoU） values reach 0.845 and 0.779， respectively. In the CAMUS dataset with blurred boundaries， the average Dice and IoU values reach 0.883 and 0.796， respectively， both of which outperform current mainstream methods. Additionally， ablation experiments further validate the effectiveness of each module. MCNet uses the MoE mechanism to dynamically adjust the fusion weights between global and local features in real time， enhancing structural detail integrity while maintaining global perception， thereby providing an efficient and robust solution for multi-modal cardiac image segmentation.