Aiming at the problem of poor visual effects of block-based compressed sensing reconstructed images at low sampling rates， a compressed sensing image reconstruction method that fused Spatial Location and Structure Information （SLSI） was proposed. Firstly， observations were linearly mapped to obtain initial estimated values of image blocks. Then， based on block grouping reconstruction branch and whole image reconstruction branch， the spatial location information and structure information of the image were extracted， enhanced and fused. Finally， weighted strategy was used to fuse the outputs of the two branches to obtain final reconstructed whole image. In the block grouping reconstruction branch， reconstruction resources were allocated according to the data characteristics of the image blocks. In the whole image reconstruction branch， information exchange between adjacent image block pixels was mainly carried out through bilateral filtering and structural feature interaction module. Experimental results show that compared with compressed sensing reconstruction methods based on non-iterative Reconstruction Network （ReconNet） and Multi-scale Reconstruction neural Network with Non-Local constraint （NL-MRN）， due to the combination of the image prior with strong autocorrelation between pixels， when sampling rate is 0.05， the average Peak Signal-to-Noise Ratio （PSNR） and Structural SIMilarity index （SSIM） of the proposed method on the test image data commonly used in the compressed sensing field increase 2.617 5 dB and 0.105 3 respectively， and the visual effects of reconstructed images are better.