Few-shot image classification aims to perform image classification on new categories with only a small amount of labeled training data. However, it is difficult to complete such a task under the existing conditions. Therefore, the current few-shot learning methods are built on the paradigm of transfer learning, where the core idea is to learn a priori that can solve unknown new tasks. The idea of meta-learning is fast learning, which is similar to the idea of few-shot learning. Therefore, conventional few-shot learning methods generally adopt a meta-training approach with episodic training to construct knowledge prior. However, research work indicates that an embedding model with powerful feature representations is simpler and more effective than the existing sophisticated few-shot learning methods. Inspired by this insight, we propose a few-shot image classification method based on the transductive clustering optimization learning. First, memory block is introduced to store the internal feature structure of each category, namely comprehensive representation of various features. Second, during the training process, sample features are compared with the memory content to further expand the differences between different categories and improve the performance of feature extractor. Finally, in the transductive few-shot setting, a clustering optimization module has been introduced to select appropriate samples for clustering to alleviate the fundamental problem of sample scarcity. A large number of experimental results show that the proposed few-shot image classification method based on the transductive clustering optimization learning effectively improves the classification accuracy under various training conditions.