The four-dimensional information of space and angle can be obtained by one-time imaging of light field. The existing methods are mostly used for the light field image of small baseline scene in the angular super-resolution reconstruction, and there are some phenomena such as blur when reconstructing the large baseline scenes. At the same time, the reconstruction effect of the occlusion area is poor in the process of light field reconstruction, and the long-distance spatial relationship is difficult to capture. To solve this problem, a sparse light field intensive reconstruction method combining depth clues and geometric structure is proposed. This method uses spatial pyramid pool to extract multi-scale features, which can preserve the texture details and high frequency information of images better. By introducing void convolution and dense connection in the depth estimation module, the receptive field is expanded and the accuracy of depth estimation of large baseline scene is improved. The view refinement module is used to optimize the image and reconstruct the occlusion area while preserving the parallax structure. Experimental results show that the proposed method solves the problem of largebaseline scene optical field reconstruction well, and exceeds other algorithms in the large-baseline scene data set, with PSNR increased by 2 dB and SSIM increased by 0. 018. The quality of reconstructed images is superior to the existing algorithms.