Hyperspectral compressive imaging has shown remarkable advancements through the adoption of deep unfolding frameworks, which integrate the proximal mapping prior into the data fidelity term to formulate the reconstruction problem. However, existing technologies still face challenges in effectively capturing spatial-spectral features during the iterative deep prior learning stage, leading to unsatisfactory performance degradation. To address this issue, we propose a deep unfolding 3D non-local transformer (3DNLT) network for hyperspectral compressive imaging. A learnable half-quadratic splitting (HQS) algorithm is utilized to iteratively update the linear projection. Furthermore, a 3D non-local attention ushaped transformer is presented as the deep proximal mapping prior module to obtain the spatial-spectral long-range dependency features, leading to enhance the network's ability to capture fine-grained hyperspectral and spatial details. Experimental results on both synthetic and real hyperspectral image reconstruction have demonstrated the superior performance of the 3DNLT network compared to state-of-the-art methods.