With the rapid development of 5G and IoT technologies, edge computing networks face dynamic challenges in task offloading and resource allocation complexity. In this paper, we propose a Deep Deterministic Policy Gradient (DDPG)-based optimization framework for multi-user multi-edge-server scenarios. The dynamic task offloading and resource allocation problem is formalized as a Markov Decision Process (MDP), defining a state space incorporating server resources, user locations, and task states, along with a hybrid action space combining continuous resource allocation and discrete offloading decisions. The key innovation lies in designing a reward function maximizing task completion volume, implementing dynamic policy optimization through an Actor-Critic network architecture, and enhancing stability via experience replay with target network soft updates. Simulation results demonstrate that the Deep Deterministic Policy Gradient algorithm achieves significantly higher average task completion rewards compared to other algorithms, along with substantially lower reward standard deviation.