As the Metaverse evolves with developments in AI, semantic communication, edge computing, and blockchain, it faces the challenge of adapting to dynamic environments and increasing communication and computational needs. In this paper, we propose an edge blockchain architecture for the dynamic Metaverse that relies on unmanned aerial vehicle (UAV) swarms comprising a central collection UAV and several UAV servers. We optimize the selection of UAV servers and computing resource allocation to match the characteristics of tasks from different semantic environments with their respective communication and computational needs. To solve the problem, we develop a particle swarm optimization-based collection-edge mobility algorithm (PSO-CEMA) to optimize UAV server deployment and adapt their positions to meet the task requirements. Additionally, we present a dual-queue system and a Lyapunov drift function-based dynamic programming task allocation algorithm (LDF-DPTAA) to facilitate timely task allocation with reduced complexity. Furthermore, to optimize the allocation of computational resources dynamically, we adopt lifelong learning and design a collection-edge joint training and processing algorithm (LL-CJTPA). Simulation results show that our approach effectively optimizes edge servers' positions and task allocation, reduces the collection UAV's training time in new task environments, and enhances the stability and efficiency of the network in dynamic settings while reducing congestion.