In recent years, deep learning has rapidly advanced, significantly impacting various domains and becoming an essential driver in technological innovation systems. The integration of deep learning into these systems fosters new possibilities for collaborative innovation, enabling enhanced decision-making, intelligent automation, and data-driven research. This paper explores the application of deep learning in technological innovation systems, analyzing how it catalyzes collaborative innovation across industries, research institutions, and government agencies. By examining case studies and recent developments, this study reveals the mechanisms that facilitate collaboration, knowledge sharing, and resource integration, all essential for advancing innovation capacity. Experimental findings demonstrate that a well-structured collaborative mechanism amplified by deep learning can improve innovation efficiency and resource utilization. This paper contributes insights into designing and optimizing innovation systems that leverage deep learning, offering pathways for future research and application in sustainable and competitive innovation ecosystems.