Accurate detection of bridge cracks is crucial for ensuring traffic safety, but the slender morphology of cracks and complex backgrounds bring great challenges to high-precision detection. A bridge crack segmentation network, WMCNet, was proposed, which fused wavelet transform and the Mamba architecture to enhance crack detection performance in complex scenarios by coordinating frequency-domain information and spatial context. A wavelet Mamba interaction module was adopted to decompose features into multiple frequency bands, and the Mamba structure was utilized to enhance its long-range dependencies to capture global information. An edge-enhancement feature fusion module was designed, which performed deep fusion and boundary optimization on multi-scale features in skip connections by introducing a fuzzy graph inference mechanism that utilized graph networks and fuzzy logic. To further optimize the segmentation results, the network training adopted a hybrid loss function that combines BCE and Dice losses to ensure pixel-level accuracy, and an adaptive loss module was introduced to enhance the model's feature representation capability. Experiments demonstrate that WMCNet achieves mIoU scores of 88.58%, 86.53%, and 83.34% on the self-built CQBCD dataset and the public crack datasets DeepCrack and CrackTree260, respectively. Compared with nine other models, the proposed network exhibits superior performance.