Cloud-edge collaboration scheduling has emerged as a solution to alleviate cloud workloads by pushing tasks to edge nodes. However, the existing deep-learning-based scheduling methods are challenging to scale to large-scale environments due to high memory overhead during model training. We propose a hierarchical cloud-edge collaborative task scheduling framework, HSC, to address the scalability issue. HSC organizes cloud servers and edge nodes into areas and employs a two-level hybrid scheduling workflow. The inter-area scheduling uses lightweight load-balanced rules to distribute tasks efficiently, while the intra-area scheduling applies a deep-learning-based model for high-quality decisions. HSC breaks down large-scale scheduling problems into smaller area-level scheduling problems, significantly reducing the amount of training memory required. Experiments demonstrate that HSC reduces task response time by 2.8% to 3.9% and service-level agreement violation ratios by 11.5% to 20.8% compared to the cutting-edge deep-learning-based scheduling model GOSH. HSC is capable of supporting large-scale environments with 2000 cloud and edge nodes, which is 10 times larger than GOSH under the same memory constraint.