In recent years， many studies on conversational recommender systems use pre-trained language models as unified frameworks， aiming to address the issue of inadequate coordination among modules in traditional multi-module architecture. However， these methods cannot fully exploit the coordination between tasks and fail to capture potential structured information in the input effectively. These problems weaken the performance of conversational recommender systems in real-world application scenarios significantly. Therefore， a Conversational Recommender System with Structure-enhanced hierarchical task-oriented prompting strategy named SetaCRS was proposed. In SetaCRS， a heterogeneous graph attention neural network was used to model the sequence co-occurrence information in historical conversations between the user and the system. In addition， hierarchical global task descriptions and specific sub-task descriptions were constructed to help the model capture and utilize the relationship between the current sub-task and the overall task sequence. Experimental results on two public datasets， DuRecDial and TG-ReDial， show that compared with UniMIND （Unified MultI-goal conversational recommeNDer system）， SetaCRS achieves improvements of 8.53% and 1.55% in semantic F1， respectively， and improvements of 3.02% and 9.54% in Mean Reciprocal Rank （MRR）@10， respectively. It can be seen that the task dependencies and conversational structured information captured by SetaCRS improve recommendation accuracy and response quality effectively.