We　consider　the　problem　of　hyperspectral　image　(HSI)　reconstruction,　which　aims　to　recover　3D　hyperspectral　data　from　2D　compressive　HSI　measurements　acquired　by　a　coded　aperture　snapshot　spectral　imaging　(CASSI)　system.　Existing　deep　learning　methods　have　achieved　acceptable　results　in　HSI　reconstruction.　However,　these　methods　did　not　consider　the　imaging　system　degradation　pattern.　In　this　paper,　based　on　observing　the　initialized　HSIs　obtained　by　shifting　and　splitting　the　measurements,　we　propose　a　dynamic　Fourier　network　based　on　degradation　learning,　called　the　degradation-aware　dynamic　Fourier-based　network　(DADF-Net).　We　estimate　the　degradation　feature　maps　from　the　degraded　hyperspectral　images　to　realize　the　linear　transformation　and　dynamic　processing　of　the　features.　In　particular,　we　use　the　Fourier　transform　to　extract　the　HSI　non-local　features.　Extensive　experimental　results　show　that　the　proposed　model　outperforms　state-of-the-art　algorithms　on　simulation　and　real-world　HSI　datasets.　The　source　code　is　available　at:　https://github.com/CISMOLab/DADF-Net　IEEE