Mobile Edge Computing (MEC) is a new paradigm to address massive content access on mobile networks to support the fast-growing Internet of Things (IoT) services and applications. However, inappropriate cache placement and utilization and requests for cached data at different times are highly variable. Nevertheless, most current optimization approaches lack the adaptive ability to orchestrate dynamic caching environments, and although many studies use online deep learning approaches, many challenges remain. This paper synthesizes the value of cached content and the cost of transmission links to derive a comprehensive utility function that can meet both the performance and link cost requirements of edge computing caching systems by dynamically changing the weight values. To improve the timeliness of the caching policy and the efficiency of deep learning, we propose a collaborative two-stage deep reinforcement learning (CDRL) framework to design the caching mechanism; CDRL cleverly combines double-deep reinforcement learning (DDRL) with deep sarsa. Experimental results show that the proposed approach can effectively improve the performance in terms of cache hit rate, service latency, and link cost.