The densely sampled light field can provide immersive visual experience and have extensive applications in areas such as 3D reconstruction and virtual reality. Light field reconstruction technology can obtain densely sampled light fields from sparsely sampled light fields, but when the baseline of the light field image increases, it will cause occlusion on edges and textured duplicate areas blur in the reconstructed view. In this paper, we propose a light field reconstruction network based on spatial-anglular decouple and fuse, it can make full use of the depth information and spatial information contained in the light field image and solve the reconstruction problems of the complex regions. The multi-branch input is used to supply the multi-directional information of the light field to solve the occlusion problem. In each branch, multi-scale features are extracted by atrous spatial pyramid pooling module to obtain disparity information and context information for the large receptive fields. The spatial-angular decouple and fuse module is designed to explore the spatial information, guiding the light field angle super-resolution reconstruction so as to obtain the reconstruction view with clearer texture duplicate region. Experimental results show that the average PSNR and SSIM reached 39.73 dB and 0.965 on the HCI, HCI old and 30 scenes datasets,showing high accuracy and outperforming the compared method.