Energy resources have acquired a strategic significance for economic growth and social welfare of any country throughout the history. Therefore, the prediction of crude oil price fluctuation is a significant issue. In recent years, with the development of artificial intelligence, deep learning has attracted wide attention in various industrial fields. Some scientific research about using the deep learning model to fit and predict time series has been developed. In an attempt to increase the accuracy of oil market price prediction, Long Short Term Memory, a representative model of deep learning, is applied to fit crude oil prices in this paper. In the traditional application field of long short term memory, such as natural language processing, large amount of data is a consensus to improve training accuracy of long short term memory. In order to improve the prediction accuracy by extending the size of training set, transfer learning provides a heuristic data extension approach. Moreover, considering the equivalent of each historical data to train the long short term memory is difficult to reflect the changeable behaviors of crude oil markets, a very creative algorithm named data transfer with prior knowledge which provides a more availability data extension approach (three data types) is proposed. For comparing the predicting performance of initial data and data transfer deeply, the ensemble empirical mode decomposition is applied to decompose time series into several intrinsic mode functions, and these intrinsic mode functions are utilized to train the models. Further, the empirical research is performed in testing the prediction effect of West Texas Intermediate and Brent crude oil by evaluating the predicting ability of the proposed model, and the corresponding superiority is also demonstrated. (C) 2018 Elsevier Ltd. All rights reserved.