Lane change plays a vital role in traffic safety. Accurately predicting the driver's lane change behavior can significantly improve driving safety. In this study, a hybrid neural network based on fully connected neural network and recurrent neural network is proposed to accurately predict lane change behavior. And a dynamic time window is proposed to extract lane change features including driver physiological data and vehicle kinematics data. Finally, the effectiveness of the proposed model is verified by the data in real traffic scenarios. In addition, the proposed model is compared with five other predictive models. The results show that the proposed model has higher accuracy and perspective time than other models.