The transition from traditional learning to practice-oriented programming learning will bring learners discomfort. The discomfort quickly breeds negative emotions when encountering programming difficulties, which leads the learner to lose interest in programming or even give up. Emotion plays a crucial role in learning. Educational psychology research shows that positive emotion can promote learning performance, increase learning interest and cultivate creative thinking. Accurate recognition and interpretation of programming learners' emotions can give them feedback in time, and adjust teaching strategies accurately and individually, which is of considerable significance to improve effects of programming learning and education. The existing methods of sensor-free emotion prediction include emotion prediction based on keyboard dynamic, mouse interaction data and interaction logs, respectively. However, none of the three studies considered the temporal characteristics of emotion, resulting in low recognition accuracy. For the first time, this paper proposes an emotion prediction model based on time series and context information. Then, we establish a Bi-recurrent neural network, obtain the time sequence characteristics of data automatically, and explore the application of deep learning in the field of Academic Emotion prediction. The results show that the classification ability of this model is much better than that of the original LSTM (Long-Short Term Memory), GRU (Gate Recurrent Unit) and RNN (Re-current Neural Network), and this model has better generalization ability.