In this study, we propose a depth estimation method for phase-coding light field based on a lightweight convolutional neural network. This method aims to solve the problems of low accuracy for depth values caused by the insufficient texture of a measured object in traditional light field depth value estimation and high computational loads caused by high-dimensional light field data. In addition, a new phase-coding light field dataset is proposed. This novel method exploits the information of horizontal and vertical perspectives in phase-coding light field to extract the features using full convolutional networks and deepening average pooling. Furthermore, the central view is used as a guide to fuse the horizontal and vertical features and acquire the depth map. The experimental results demonstrate that the proposed method can generate high-accuracy depth maps, while number of parameters and computation time in generating such maps are, respectively, 27.4% and 41.2% of those of a typical light field depth estimation network. Thus, the proposed method has a higher efficiency and real-time performance than the traditional approach.