As scientific research is increasingly dependent on fast data transmission, the requirements for link resource planning and operation management of scientific research networks are more demanding. Considering the actual needs of scientific research networks, a good link traffic prediction model can help the network operators make fast decisions on link resource scheduling more effectively with the assistance of flexible network control technology such as SDN. The existing prediction model has ignored the current network traffic is more diversified and more complex in fine-grained features. This study proposes a new link traffic prediction model based on the improved LSTM model to meet the management needs of scientific research networks. Composed of AutoEncoder (AE), Bi-LSTM model, unidirectional LSTM model, and fully-connected layers, it can greatly improve the extraction ability of traffic features and better explore the dependent manners among data features at different time. The model is verified by using the associated node data of a link randomly selected from the real production environment of the national backbone network of Science and Technology Daily—CSTNet. The experimental results show that the prediction results of the model accord with the real change trend of traffic, and the residual between the predicted value and the observed value is small, which means the model can well fit the existing traffic of the scientific research network.