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Because of the difficult to analyze the evolution law of CNC machine tools accuracy through mathematical modeling, a method of motion accuracy modeling and prediction based on sequential deep learning network was proposed. A deep learning network was presented based on the long shortterm memory (LSTM). Using the principle of phase space reconstruction, the sequence input vector of the model was constructed. The optimal parameters of the model, such as number of hidden layer and number of hidden layer node were determined based on multilayer grid search algorithm. The model was trained with BPTT method. The mutual information before and after the precision time series was mined with data driven. The temporal and spatial characteristics of the motion accuracy series were automatically extracted through the deep learning network. Finally, the declining trend of motion accuracy was predicted by the model. The experiments results showed that the prediction model based on the sequential deep learning network could predict properly the evolutionary trends and regularity of the precision. The maximum relative error of prediction was not more than 796%. The prediction accuracy of the method was better than that of the traditional methods. The method was helpful for evaluating the reliability of NC machine tools and ensuring the machining accuracy.