Meta-learning has achieved promising results in low-resource text classification, which aims to identify target classes by transferring knowledge from source classes through a series of small tasks called episodes. However, the current meta-learning algorithms that solely rely on learning from meta-training tasks may struggle to generalize well to meta-testing tasks. To address this problem, we propose a method called Retrieval-Augmented Meta Learning (RAML) that utilizes external knowledge to compensate for the performance degradation when meta-training tasks do not adequately support meta-testing tasks. RAML first utilizes a retriever to retrieve knowledge relevant to the query from an external corpus, and then employs the Multi-View Passages Fusion Network to integrate the retrieved knowledge for performing few-shot classification. This network can effectively combine the probability distributions of classifications obtained from multiple messages by considering the importance of different messages. Furthermore, inspired by knowledge distillation, we iteratively train the retriever model using the synthetic labels generated by the aforementioned network. Extensive experiments demonstrate that RAML significantly outperforms current state-of-the-art baselines(e.g., ChatGPT).