In　the　coded　aperture　snapshot　spectral　compressive　imaging　(CASSI)　system,　hyperspectral　image　(HSI)　reconstruction　methods　are　employed　to　recover　3D　signals　from　2D　compressive　measurements.　Among　these　methods,　although　the　deep　unfolding　networks　show　the　advantages　of　interpretability　and　high　efficiency,　they　still　have　the　limitations　of　insufficient　feature　utilization　and　low　efficiency　of　information　interaction　between　stages.　To　solve　these　problems,　in　this　paper　two　well-designed　techniques,　dubbed　as　dual-domain　feature　learning　and　feature　memory-enhanced　module,　are　introduced　into　a　deep　unfolding　network.　The　former　is　proposed　to　enhance　the　representation　ability　of　deep　networks,　while　the　latter　is　designed　to　efficiently　promote　the　cross-stage　information　interaction.　Extensive　experimental　results　validate　the　efficiency　and　effectiveness　of　the　proposed　method.