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The vehicle speed, distance, driving direction and other factors on the expressway are all dynamic changes, and the collected target vehicle status feature data may have noise due to the interference of the external environment, which leads to errors in the prediction of vehicle lane change trajectory. Therefore, a prediction model of expressway vehicle lane change trajectory based on long-term and short-term memory network is proposed to effectively predict the lane change trajectory of expressway vehicles, improve vehicle driving conditions, and ensure their safe operation. Collect target vehicle traffic data through laser radar, GPS and other devices, reasonably combine them into target vehicle state observation eigenvectors, and construct corresponding eigenvector matrix. The constructed target vehicle state observation eigenvector matrix is used as the input of 1-layer convolutional neural network. After extracting the potential characteristics of target vehicle state observation eigenvectors, the output results of 1-layer convolutional neural network are effective input of two-way short-term memory network, After countless model trainings, the predicted lane change trajectory of the target vehicle is output. The experimental results show that the model can effectively predict the lane changing trajectory of highway vehicles, and the predicted trajectory has extremely low horizontal and vertical coordinate errors, which can obtain ideal prediction results for lane changing trajectories of highway vehicles.