The　hyperspectral　image　(HSI)　compressive　imaging　field　has　experienced　significant　progress　in　recent　years,　especially　with　the　emergence　of　deep　unfolding　networks　(DUNs),　which　have　demonstrated　remarkable　advancements　in　reconstruction　performance.　However,　these　methods　still　face　several　challenges.　Firstly,　HSI　data　carries　crucial　prior　knowledge　in　the　feature　space,　and　effectively　leveraging　these　priors　is　essential　for　achieving　high-quality　HSI　reconstruction.　Existing　methods　either　neglect　the　utilization　of　prior　information　or　incorporate　network　modules　designed　based　on　prior　information　in　a　rudimentary　manner,　thereby　limiting　the　overall　reconstruction　potential　of　these　models.　Secondly,　the　transformation　between　the　data　and　feature　domains　poses　a　significant　challenge　for　DUNs,　leading　to　the　loss　of　feature　information　across　different　stages.　Existing　methods　fall　short　in　adequately　considering　spectral　characteristics　when　utilizing　inter-stage　information,　resulting　in　inefficient　transmission　of　feature　information.　In　this　paper,　we　introduce　a　novel　deep　unfolding　network　architecture　that　integrates　local　non-local　and　low-rank　priors　with　spectral　memory　enhancement　for　precise　HSI　data　reconstruction.　Specifically,　we　design　innovative　modules　for　local　non-local　and　low-rank　priors　to　enrich　the　network＇s　feature　representation　capability,　fully　exploiting　the　prior　information　of　HSI　data　in　the　feature　space.　These　designs　also　help　the　overall　framework　achieve　superior　reconstruction　results　with　fewer　parameters.　Moreover,　we　extensively　consider　the　spectral　correlation　characteristics　of　HSI　data　and　devise　a　spectral　memory　enhancement　network　module　to　mitigate　inter-stage　feature　information　loss.　Extensive　experiments　further　demonstrate　the　superiority　of　our　approach.