Few-shot learning is a challenging task that aims to train a classifier with very limited training samples. Most existing few-shot learning methods mainly focus on mining knowledge from limited training samples as much as possible and ignore the learning order. Inspired by human learning, people select useful knowledge and follow a learning path to enhance their learning ability. In this paper. we propose a novel few-shot learning model called dynamic knowledge path learning (DKPL) to guide the few-shot learning task to learn useful selected knowledge with suitable learning paths. Specifically, we simultaneously consider the importance, direction, and diversity of knowledge and propose a dynamic path learning strategy in the dynamic path construction module. Furthermore, we design a new learner to absorb knowledge, step by step, according to each class’s learning path in the knowledge path propagation module. As far as we know, this is the first few-shot learning work to consider dynamic path learning to improve classification accuracy. Experiments and visual case studies demonstrate the effectiveness and superiority of the DKPL model on four real-world image datasets.