The light field image can record the light information of different position and direction in space simultaneously, which provides rich information for estimating accurate depth map. However, in complex scenes such as occlusion and repeated texture, the lack of feature extraction will lead to the loss of detail in depth map. An optical field depth estimation network based on correction convolution is proposed to make full use of the rich structural information for optical field images to improve the depth estimation of complex areas such as occlusion. The occlusion mask is generated by using the initial disparity map and subaperture image, and the spatial information of the occlusion area is perceived by correcting convolutional discrimination and encoding, and multi-scale features are combined to supplement the edge details that are easily lost.The spatial attention mechanism is used to give more weight to the occlusion area, eliminate redundant information and optimize the subpixel cost body globally. Experimental results show that average MSE and BadPix (ε=0.03) of the proposed method on 4D optical field reference platform are 0.951 and 4.261, respectively. The proposed method can achieve depth estimation with minimum error in most scenes, and shows high robustness to the occlusion area, which is better than other algorithms.