Abstract: To assist learners in maintaining the continuity of online learning and guiding the implementation of the optimal learning path, the intelligent tutoring system (ITS) needs to detect the tendency of learners to withdraw from learning in time and take corresponding intervention measures at the right time. Therefore, online learning session dropout prediction research is necessary. However, compared with traditional course dropouts, session dropouts occur more frequently and with shorter single study sessions. Session dropout requires the accurate prediction of learning session dropout state based on limited learning behavior feature data. Therefore, the fragmentation of learning behavior and the immediacy and accuracy of prediction results are the challenges and difficulties of learning session dropout prediction tasks. For the session dropout prediction task, we propose a unified online learning session dropout prediction model (Uni-LSDPM). Based on the pre-training and fine-tuning paradigm, Uni-LSDPM uses a multi-layer Transformer structure. In the pre-training stage, a bidirectional attention mechanism is used to learn the representation of sequence of learners’ continuous behavioral interaction features. In the fine-tuning stage, a sequence-to-sequence (Seq2Seq) attentional mechanism is used to learn the sequence combination of the learner’s continuous behavioral interaction features and dropout state. The proposed model is pre-trained and fine-tuned based on the EdNet public dataset, and the best prediction effect is obtained through ablation experiment. Comparative experiments are conducted based on multiple datasets. Experimental results show that Uni-LSDPM outperforms existing models in terms of AUC and ACC, and prove that the model has certain robustness and scalability.