Abstract: Existing auxiliary diagnosis methods based on Convolutional Neural Network (CNN) are difficult to achieve ideal accuracy in pneumoconiosis screening and staging tasks. This paper proposes a multi-branch structure model for pneumoconiosis screening and staging based on chest X-ray images, namely MBM-Mamba, and proposes a new six-direction scanning strategy under the framework of 2D-Selective-Scan (SS2D). The diagonal scanning is used to explicitly capture the two-dimensional local dependency with linear time complexity. Then, a Detail Enhancement Module (Dem) is constructed by integrating prior information into CNN, thus forming a local feature extraction module (CNN-Mamba), which significantly improves the expression ability of subtle lesion information.In addition, the MBM-Mamba model designs a global feature extraction module based on the multi-head self-attention mechanism, which effectively enhances the ability to capture global context. The MBM-Mamba model integrates the local and global feature extraction modules by using multi-path residuals, and realizes the synchronous hierarchical fusion of cross-structural features, so that the model can better understand the overall distribution of lung lesions and the degree of fibrosis. Verified on 1760 chest X-ray images of real anonymous patients, the accuracy of the MBM-Mamba model reaches 0.786, and the F1 score is 0.790. Both indicators are better than the existing models.