In　the　Coded　Aperture　Snapshot　Spectral　Imaging　(CASSI)　systems,　hyperspectral　images　(HSIs)　reconstruction　methods　are　employed　to　recover　3D　signals　from　2D　compressive　measurements.　Among　these　methods,　deep　unfolding　networks　exhibit　the　benefits　of　interpretability　and　high　efficiency,　but　they　still　have　some　notable　shortcomings.　Firstly,　existing　methods　primarily　exploit　the　spatial-spectral　domain　information　of　HSIs,　neglecting　exploration　of　the　frequency　domain,　which　is　also　beneficial　to　3D　HSIs.　Secondly,　current　unfolding　networks　have　limited　utilization　of　information　between　different　stages,　failing　to　fully　explore　their　relevance　and　thereby　limiting　the　effectiveness　of　the　overall　framework.　To　address　these　issues,　in　this　paper,　we　propose　an　integrated　framework　with　dual-domain　feature　fusion　and　multi-level　memory　enhancement.　Specifically,　the　former　represents　the　first　attempt　to　utilize　frequency　domain　information　in　the　feature　space　of　HSIs　overcoming　the　limitation　of　spatial-spectral　domain　features　and　thereby　improving　the　data　expression　ability　of　the　network　by　extracting　dual-domain　features.　Simultaneously,　our　verification　experiments　also　show　that　the　proposed　dual-domain　feature　representation　can　indeed　extract　complementary　feature　information　in　HSIs.　Moreover,　the　latter　aims　to　use　the　structural　characteristics　of　the　U-Net　network　to　fully　extract　the　correlation　of　information　between　different　stages　by　designing　a　multi-level　memory　enhancement　network.　Extensive　experimental　results　on　various　datasets　validate　the　superiority　of　the　proposed　approach　in　both　subjective　and　objective　outcomes.　Our　proposed　method　achieves　an　average　of　0.4dB　improvement　over　the　best　counterpart　method.　And　the　code　can　be　obtained　from　the　link:　https://github.com/yingyangke/DFFMM.