In this thesis, we shall discuss the unsupervised meta-learning. Building a general-purpose AI demands an intelligent system capable of learning a broad range of knowledge with modest data and transferring the learned knowledge to the concrete case. Meta-learning is introduced to tackle this problem. Enabled by the common feature between meta-learning and unsupervised learning that they both learn a learning procedure that is more efficient and effective than learning from scratch, we propose the Symbolic Vector coupling Energy-Based Model (SVEBM) to implement unsupervised meta-learning by exploiting the structural difference between unsupervised and supervised meta-learning. From the probabilistic point of view, we illustrate these approaches as graphical models.