6G is envisioned to be characterized by ubiquitous connectivity, extremely low latency, and enhanced edge intelligence. However, enriching 6G with these features requires addressing new, unique and complex challenges specifically at the edge of the network. In this paper, we propose a wireless digital twin edge network model by integrating digital twin with edge networks to enable new functionalities such as hyper-connected experience and low-latency edge computing. To efficiently construct and maintain digital twins in the wireless digital twin network, we formulate the edge association problem with respect to the dynamic network states and varying network topology. Furthermore, according to the different running stages, we decompose the problem into two sub-problems, including digital twin placement and digital twin migration. Moreover, we develop a Deep Reinforcement Learning (DRL) based algorithm to find the optimal solution to the digital twin placement problem, and then use transfer learning to solve the digital twin migration problem. Numerical results show that the proposed scheme provides reduced system cost and enhanced convergence rate for dynamic network states.