Automatic driving vehicles need to have the ability to predict the intentions of changing lanes to ensure driving safety in mixed traffic.In order to predict the intention as early as possible, a prediction model based on Morgrifier LSTM network is proposed. First, the S-G (Savitzky?Golay) filter is used to filter the noise reduction of the natural driving data set NGSIM (Next Generation Simulation), and the track sequence of different lengths of time is marked by changing lane to the left, right, and driving straightly.Select the input model of vehicle motion information and environmental information. Finally, the softmax function is used to classify the intention. The result shows that the prediction accuracy of the model is higher than SVM and LSTM under different prediction times, and the closer to the lane-changing point, the higher the prediction accuracy. At 1.0 s and 2.5 s, the prediction accuracy is 93.83% and 81.30% respectively. The proposed model has pleasurable accuracy and predictability. It can provide technical support for automatic driving vehicles to identify lane-changing intention as early as possible.