Network virtualization will be an enabler for intelligent energy-aware network deployment. Current networks are designed for peak loads, resulting in inadequate resource utilization and energy consumption waste. Due to current power consumption insensitiveness of network equipment to traffic load, resource consolidation becomes an effective energy-saving technology. Based on the virtual network mapping characteristics and the substrate network energy consumption, this paper presents a multi-objective decision-making model that is also a mixed integer programming model for energy efficient virtual network embedding. To address high time complexity in solving the mixed integer programming model, the paper analyzes the dynamic characteristics of the virtual network mapping, constructs virtual network mapping dictionary database and proposes a method for training substrate network resource utilization, as well as an algorithm which actively hibernates the substrate nodes and links. By this method, the virtual network can be embedded in a smaller set of substrate nodes and links, which helps to increase the number of hibernating substrate nodes and links, and achieve energy-effective virtual network mapping. Simulation results demonstrate the proposed method can effectively improve the number of hibernating nodes and links of substrate network, and significantly reduce energy consumption of substrate network.