Background network traffic generation is critical to replicating the real network environment in Cyber Range. But how to sufficiently extract the spatio-temporal features of traffic and generate superior background network traffic are still problems for the Cyber Range. In this paper, we propose a background network traffic generative model, DBWE-Corbat. Our solution relies on intelligent feature extraction based on the DB-WE dynamic word embedding method. Which consists of Doc2Vec and two Bidirectional Long Short-Term Memory (Bi-LSTM) layers. Specifically, first we convert the traffic feature tuple data into a static word vector. Then, we capture the spatio-temporal features of the traffic for characterization. Finally, we generate high-quality and numerous background network traffic by learning the feature distribution of small samples based on the contrastive learning model SimCSE. Extensive experiments show that our approach can generate high-quality traffic data. It meets the requirements of cyber range construction compared to other traffic generation methods.