In recent years, with the profound changes in the global energy supply and demand structure, large oil tankers have become key participants in international energy transportation, playing an important role in ensuring the safety and stability of energy transport. Accurate prediction of tanker routes has become a crucial task to guarantee this. However, existing long-distance route prediction methods rarely consider trajectory consistency during data preprocessing, and recursive predictions suffer from error accumulation, leading to low prediction accuracy. To address these issues, our study proposes a new route prediction framework. The framework introduces an IMO and MMSI matching method during data preprocessing to resolve inconsistencies in historical trajectory data caused by changes in MMSI. Furthermore, to better address the issue of continuous position drift in trajectories, this study proposes an outlier cyclic deletion method. After extracting OD trajectory data based on the buffer zone, this study combines the Transformer model with the Long Short-Term Memory (LSTM) network model, leveraging their strong ability to capture long-term dependencies in time-series data. An Encoder-LSTM architecture-based route prediction model is then constructed, alleviating the decline in prediction accuracy caused by error accumulation. Experimental results show that the proposed framework significantly improves the accuracy and reliability of route prediction.