The process of feeding wolfberry into betel nut still needs to be accomplished manually by workers, which has low production efficiency and food safety issues. To address this problem, betel nut pose recognition and positioning system based on structured light three-dimensional (3D) vision is designed. First, a digital projector projects blue sinusoidal fringe patterns onto betel nuts, and once the deformed fringe images are acquired, the computer performs 3D reconstruction to obtain a high-precision betel nut point cloud. Subsequently, two-dimensional (2D) image and 3D point cloud information are fused, and the proposed feature line method is used to estimate the betel nut pose parameters. Finally, the center of the betel nut cavity is positioned as the feeding point, which is subsequently converted to the base coordinate system of a robot arm according to hand-eye calibration to complete automatic feeding. Experiments on pose recognition and localization were conducted using 500 betel nuts, and both processing time and classification accuracy were evaluated. Results show that the processing time of one betel nut is 0.39-0.59 s. The overall recognition accuracy is 95.6%. The localization error for the feeding point is within 0.25 mm, which is lower than the 0.3 mm required for feeding. This demonstrates that the proposed method can effectively solve the problem of attitude recognition and positioning of freely placed targets within complex shapes and has high positioning accuracy and good stability to meet the actual production requirements.