A multi-scale spatial-spectral feature collaborative hyperspectral image pansharpening method was constructed in order to enhance the spatial representation capability of hyperspectral images while preserving spectral consistency. A bidirectional pyramid multi-scale alignment structure was adopted to progressively upsample the hyperspectral image and downsample the panchromatic image, generating multi-scale input pairs and achieving spatial-spectral consistency alignment at the input end. A spatial-spectral feature mining and enhancement module was constructed, and multi-scale cross-attention was employed to strengthen spatial-spectral feature extraction. Spatial-spectral feature mining together with sliding-window fusion were combined to realize deep mining and collaborative fusion of heterogeneous features. A spectral attention mechanism was introduced to enhance channel consistency, forming an extraction-mining-fusion-enhancement spatial-spectral collaborative feature flow. A progressive multi-scale spatial-spectral reconstruction module was designed to integrate multi-scale information through stage-wise reconstruction and feature compensation, achieving hierarchical spatial-spectral consistency reconstruction. Experiments were conducted on multiple hyperspectral datasets. Results demonstrate that the proposed method outperforms several existing pansharpening techniques in both spatial detail recovery and spectral fidelity. The quality of hyperspectral image pansharpening can be effectively improved by the proposed method, which exhibits superior spatial detail and spectral fidelity across different scenes.