Abstract: In recent years, edge-cloud-end collaboration (ECEC) has emerged as an efficient and promising technology for empowering various computation-intensive applications, effectively bridging the gap between data analytics and physical states. Within ECEC, there is an urgent need for a reliable and optimal task scheduling scheme to maximize resource utilization and provide satisfactory services to end users. However, existing scheduling schemes still face numerous challenges, such as the instability and complexity of network topologies, the highly dynamic nature of task priorities, and the lack of trust mechanisms among end users. This paper proposes a blockchain-assisted dynamic priority scheduling strategy capable of transmitting large-scale tasks to edge or cloud servers in dynamic scenarios. We first model the resource allocation and task scheduling problem, establish a dynamic priority assignment model, and employ the analytic hierarchy process to determine the priority of sub-tasks generated from large-scale tasks. Subsequently, we adopt a blockchain-based Lagrangian Multiplier distributed island genetic algorithm, which transforms the formulated problem into a convex optimization form to achieve optimal resource allocation under specific schemes. Furthermore, the proposed architecture incorporates a blockchain verification mechanism to enhance system stability and strengthen data privacy protection. Extensive simulation results demonstrate that, compared with several baseline schemes, the proposed solution achieves a 10% reduction in both total system delay and privacy overhead in ECEC.