In this study, we develop a novel depth estimation method of light field image based on multi-cue fusion to solve the problem of occlusion in depth estimation process. Further, we employ the constrained adaptive defocus algorithm and the constrained angular entropy metric algorithm to obtain the defocusing and consistency clues of the scene, respectively. Subsequently, we determine the initial depth and confidence of the scene. The Canny operator is used to extract the edge information of the central perspective image for enhancing the image edge contour information. The initial depth, confidence, and edge information obtained from these scenes are fused using a Markov random field to achieve high-precision depth estimation. When compared with other advanced algorithms, the proposed method is more effective in solving the occlusion issue. The obtained depth map has higher precision, better smoothing effect and better edge retention effect.