Abstract: Efficient scheduling between cloud-network resources and video tasks is crucial for the performance of video Internet of things (VIoT) applications. However, the current scheduling algorithms used in operational VIoT systems are insufficiently adaptable to differentiated task demands and highly dynamic changes in cloud-network resources, resulting in poor performance of VIoT applications. To overcome the aforementioned problem, we propose a continuous learning-based task demand understanding and scheduling method for VIoT (CLTUS) Unlike traditional heuristic or machine learning-driven scheduling algorithms, CLTUS integrates the continuous learning into the matching between cloud-network resources and video task demands. Specifically, it first employs a continuous learning framework to accurately comprehend various video task demands. Subsequently, based on the dependency relationships among video tasks, it achieves an optimal match between tasks and servers, thereby refining the scheduling of cloud-network resources. Finally, the proposed method is deployed on a software-defined VIoT experimental platform. Compared with conventional methods, CLTUS not only improves the average processing efficiency of video tasks by 127.73% but also increases the balanced utilization rate of cloud-network resources to 67.2% on average, effectively improving the performance of VIoT applications.