Aiming at the problem of limited computing and storage resources of edge devices in the edge computing environment, we discuss efficient edge-cloud collaborative task scheduling and resource caching strategies, and study the mechanism of the self-organizing labor division swarm intelligent algorithm model. On this account, the edge cloud collaborative task scheduling algorithm based on bee colony labor division \textquoteleft activator-inhibitor' model (ECCTS-BCLDAI) and the edge cloud collaborative resource caching algorithm based on ant colony labor division \textquoteleft stimulus-response' model(ECCRC-ACLDSR) are proposed. The simulation results show that the proposed task scheduling and resource caching algorithm have better performance than the traditional algorithm in reducing the average task execution time, optimizing network bandwidth usage and reducing the edge-cloud collaboration cost.