Occluded person re-identification aims at retrieving holistic and occluded images of a specific identity based on occluded person images. Most existing methods incorporate external models to focus on visible body parts, which leads to high computational costs and fails to handle complex occlusions. To achieve high accuracy while maintaining low computational complexity, we propose a novel Feature Pruning and Recovery Learning with Knowledge Distillation (FPRL-KD) network. FPRL-KD is a teacher-student distillation architecture that effectively transfers the refined discriminative knowledge from the holistic branch to the occluded branch. Specifically, we devise a Feature Pruning Learning Module in the occluded branch to dynamically explore potential low-quality token features, which alleviates the interference from irrelevant information and noise in the images. Besides, we devise a Feature Recovery Learning Module that replaces these tokens with learnable embeddings to excavate robust and discriminative features. By integrating the enhanced knowledge distillation algorithm, the occluded branch is encouraged to learn from the holistic branch. Experimental results on two occluded datasets and two holistic datasets demonstrate the effectiveness and superiority of the proposed approach.