Dynamic functional connectivity (dFC) derived from fMRI captures the temporal dynamics of brain networks, where cross-frequency features provide complementary characterizations for brain disorder classification. Although existing multi-band approaches incorporate sub-band decomposition, they primarily rely on simplistic averaging or fixed-weight strategies, failing to adaptively fuse information across multiple frequency bands. To handle this limitation, we propose a dual-stream multi-band fusion network (DSMFN): 1) The frequency-domain stream employs a sub-band graph encoding-interaction module, where local graph convolution networks (GCNs) extract band-specific topological features, and lightweight convolutions replace computationally intensive attention mechanisms for data-driven band contribution allocation, followed by a global GCN to aggregate cross-band information; 2) The time-domain stream preserves local dynamic properties via residual multi-layer perceptron networks; 3) A feature-temporal dual-dimension cross-attention mechanism jointly models temporal evolution and cross-domain complementarity to adaptively integrate multi-band features with time-varying characteristics. Experiments on two distinct brain disease datasets demonstrate the effectiveness of DSMFN, achieving accuracies of 91.40% for MCI and 70.18% for ASD classification. This study provides an efficient fusion framework for multi-band dynamic brain network analysis, advancing precise diagnosis of brain disorders. Our code is available at https://github.com/WuLingBNU/DSMFN .