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 M-LSTM (morgrifier long short-term memory) network was proposed. First, the S-G (Savitzky-Golay) filter was 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 was marked by changing lane to the left, right, and driving straightly, the input model of vehicle motion information and environmental information was selected. Finally, the softmax function was used to classify the intention. The result shows that the prediction accuracy of the model is higher than SVM (support vector machine) 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.