The compressive sensing image reconstruction algorithms based on Convolutional Neural Networks could not capture long-range dependency of high-resolution images. Although Transformer can address this issue， it significantly increases the number of network parameters and the image reconstruction time. This paper proposed CHFNet， a combining image hierarchical-feature network for compressive sensing iterative-reconstruction to improve image reconstruction quality and reduce reconstruction time. CHFNet consisted of two sub-networks， sampling and reconstruction. The sampling sub-network utilized a learnable sampling matrix to provide more effective measurements for reconstruction phase. In the reconstruction sub-network， we introduced an iterative strategy using gradient descent and feature optimization operations， and proposed a lightweight CNN-Transformer hybrid architecture to model and optimize extremely fine-grained image hierarchical-feature， enhancing network’s sensing-capability and reducing computation complexity. Moreover， CHFNet achieved complete end-to-end training by jointly optimizing sampling-reconstruction process. The experimental results show that the proposed algorithm obtains satisfactory recovery performance on several public benchmark datasets. On the Urban100 dataset， the method of this paper improves the average PSNR and SSIM metrics by 0.63 dB and 0.007 6 respectively compared to the existing optimal algorithm CSformer. At 0.10 sampling rate， the average reconstruction time of CHFNet decreases 2.744 7 s， 3.551 0 s， and 4.775 0 s compared to CSformer on Set11， BSD68， and Urban100 datasets respectively.