Recently,　deep　unfolding　network　methods　have　significantly　progressed　in　hyperspectral　snapshot　compressive　imaging.　Many　approaches　directly　employ　Transformer　models　to　boost　the　feature　representation　capabilities　of　algorithms.　However,　they　often　fall　short　of　leveraging　the　full　potential　of　self-attention　mechanisms.　Additionally,　current　methods　lack　adequate　consideration　of　both　intra-stage　and　inter-stage　feature　fusion,　which　hampers　their　overall　performance.　To　tackle　these　challenges,　we　introduce　a　novel　approach　that　hybridizes　the　sparse　Transformer　and　wavelet　fusion-based　deep　unfolding　network　for　hyperspectral　image　(HSI)　reconstruction.　Our　method　includes　the　development　of　a　spatial　sparse　Transformer　and　a　spectral　sparse　Transformer,　designed　to　capture　spatial　and　spectral　attention　of　HSI　data,　respectively,　thus　enhancing　the　Transformer＇s　feature　representation　capabilities.　Furthermore,　we　incorporate　wavelet-based　methods　for　both　intra-stage　and　inter-stage　feature　fusion,　which　significantly　boosts　the　algorithm＇s　reconstruction　performance.　Extensive　experiments　across　various　datasets　confirm　the　superiority　of　our　proposed　approach.