Reconstructing　Hyperspectral　Images　(HSIs)　from　Coded　Aperture　Snapshot　Spectral　Imaging　(CASSI)　is　an　important　yet　challenging　task.　The　core　issue　lies　in　recovering　reliable　and　detailed　3D　HSI　cube　from　2D　measurement.　Deep　unfolding　framework　which　alternates　between　solving　data　subproblems　and　prior　subproblems　has　made　satisfactory　progress　in　HSIs　reconstruction　task.　However,　current　methods　do　not　fully　utilize　the　spatial　spectral　prior　of　HSIs.　To　solve　this　problem　and　further　enhance　the　spectral-spatial　representation　capabilities　in　the　prior　subproblems,　we　propose　a　Spatial-Spectral　Correlation　Transformer　Based　on　Deep　Unfolding　Framework　(SSCDUF).　Specifically,　we　introduce　a　multi-scale　Spatial-Spectral　Correlation　Fusion　Transformer　(SSCT)　module　that　simultaneously　utilize　the　similarity　and　correlation　of　spectral　features　as　well　as　local　and　non-local　spatial　features,　jointly　using　spatial　and　spectral　prior　to　enhance　feature　representation.　Moreover,　we　further　propose　an　Adaptive　Aggregation　Skip　Connection　(AASC)　module　to　adaptively　aggregate　spatial　and　spectral　features　in　multiple　scales.　Extensive　experimental　results　on　both　simulated　and　real　scenes　demonstrate　that　SSCDUF　outperforms　the　state-of-the-art　methods　in　terms　of　quantitative　metrics　while　maintaining　low　parameter　costs　and　runtime.