Deep learning (DL) has significantly advanced pattern recognition and hyperspectral image (HSI) classification owing to its strong capability for hierarchical feature representation. However, existing DL-based HSI classification methods are often limited by scarce labeled samples, high parameter complexity, and the difficulty of learning discriminative features from high-dimensional spectral-spatial data. To address these challenges, this paper proposes a 3D convolution attention-based multi-sca