With the development of three-dimensional (3D) light-field display technology, 3D scenes with correct location information and depth information can be perceived without wearing any external device. Only 2D stylized portrait images can be generated with traditional portrait stylization methods and it is difficult to produce high-quality stylized portrait content for 3D light-field displays. 3D light-field displays require the generation of content with accurate depth and spatial information, which is not achievable with 2D images alone. New and innovative portrait stylization techniques methods should be presented to meet the requirements of 3D light-field displays. A portrait stylization method for 3D light-field displays is proposed, which maintain the consistency of dense views in light-field display when the 3D stylized portrait is generated. Example-based portrait stylization method is used to migrate the designated style image to the portrait image, which can prevent the loss of contour information in 3D light-field portraits. To minimize the diversity in color information and further constrain the contour details of portraits, the Laplacian loss function is introduced in the pre-trained deep learning model. The three-dimensional representation of the stylized portrait scene is reconstructed, and the stylized 3D light field image of the portrait is generated the mask guide based light-field coding method. Experimental results demonstrate the effectiveness of the proposed method, which can use the real portrait photos to generate high quality 3D light-field portrait content.