Crude oil prices are influenced by international political, economic, military, diplomatic and other complex factors, and the frequent changes in these factors cause oil prices to exhibit random fluctuations, making crude oil investment and trading decisions difficult. Therefore, predicting oil prices accurately has become a hot research topic in the academic field of energy. However, most of the existing literature on the crude oil price forecasting predicts the value of crude oil prices rather than the change direction, and does not predict crude oil prices and volatility simultaneously, thus can't give investors sufficient information to guide their decisions. To fill this research gap, this paper proposes a new hybrid TN-LP-LSTM-SVM model combining the transition network(TN), link prediction (LP), long short-term memory model(LSTM) and support vector machine(SVM) to predict the next-day price change direction and volatility size of WTI futures more accurately, providing useful advices for investors, energy-related companies, and government personnel involved in policy decisions. Comparing the prediction accuracy of the TN-LP-LSTM-SVM model with the CNN-SVM model, LSTM and SVM for different time windows($h\in [1,50]$ and $h\in {\bm Z