Few-shot learning is to recognize novel classes with a few labeled samples per class. Although numerous meta-learning methods have made significant progress, they struggle to directly address the heterogeneity of training and evaluating task distributions, resulting in the domain shift problem when transitioning to new tasks with disjoint spaces. In this paper, we propose a novel method to deal with the heterogeneity. Specifically, by simulating class-difference domain shift during the meta-train phase, a bilevel optimization procedure is applied to learn a transferable representation space that can rapidly adapt to heterogeneous tasks. Experiments demonstrate the effectiveness of our proposed method.