Facial micro-expressions are categorized into various types based on different criteria, and typically each major category is further divided into multiple subcategories of expressions. For traditional micro-expression recognition problems, multiple subcategories of the same emotions are indiscriminately learned and verified, leading to potential misclassification, especially with negative emotions. To address the issue of intra-class variation in micro-expressions, we propose a meta-clustering learning network for micro-expression recognition called MCNet. This approach integrates the ideas of meta-learning and clustering, hierarchically clustering subcategories within a micro-expression class to generate multiple class centers for metric-based classification. The proposed method diverges from the common strategy of metric-based meta-learning algorithms, which typically use the mean feature of all samples within the same class as the class center. Furthermore, we incorporate transfer learning into the meta-learning process to jointly alleviate overfitting caused by the scarcity of micro-expression data. We conduct extensive comparative experiments based on the leave-one-subject-out protocol on three widely used micro-expression datasets. The experimental results demonstrate the competitive performance and strong generalization ability of the proposed MCNet approach.