This study addresses the limitations of cross-group communication constraints, personalized goal absence, and unconscious interactions in task scheduling-oriented multi-agent structural collaborative communication within intelligent manufacturing. We propose a Goal-Oriented (GO) learnable multi-agent structural dynamic collaboration model (GOLSC)integrating Deep Q Network (DQN) with Dijkstra's algorithm, which enhances Learning Structural Communication (LSC) by incorporating autonomous communication awareness. The framework establishes a dynamic task scheduling model by pairing each machine with an agent, while a dedicated manager agent tracks workpiece states and monitors dynamic events. By implementing GOLSC-enhanced static allocation rules and dynamic job scheduling strategies for the machine agents to select unfinished workpieces, the model achieves coordinated optimization of collaboration efficiency and production responsiveness. As the scale of the agents continues to grow, the tardiness rate of our model decreases by 20%~70% compared to traditional communication-free models and 10%~40% compared to structural communication models, and the average bandwidth occupancy rate is reduced by 10%~15%, effectively addressing the production inefficiencies caused by the lack of adaptability to dynamic events and interaction among agents in conventional intelligent manufacturing workshops.