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The decline of motion accuracy of CNC machine tools is a dynamic evolution process. To detect the potential failure risk of CNC machine tools as early as possible, the motion accuracy’s deterioration information contained in various monitoring data sequences was mined. Based on the difference and complementarity of multisource monitoring big data, a prediction method for motion accuracy’s deterioration of CNC machine tools was proposed by combining the deep gated recurrent unit (GRU) and attention mechanism. In order to overcome the defect that the traditional deep convolution neural network cannot learn the time series feature, the deep learning modeling method of motion accuracy based on deep GRU was proposed by using deep encoder-decoder structure. By datadriven, the temporal and spatial characteristics of motion accuracy and state signal time series were automatically mined to predict the change curve of motion accuracy and the deterioration trend of accuracy. At the same time, in order to enhance the information expression of main state signals and key time points, and improve the accuracy of accuracy deterioration prediction, a method of integrating attention mechanism in deep learning network was proposed. The method can establish the attention network of state parameter, calculate the correlation degree between vibration, temperature and other status signals and machine tools’ accuracy, and automatically adjust the weight of each signal. Furthermore, through establishing timeseries attention network to select the key time points of historical information of accuracy deterioration, the accuracy of longterm prediction was improved. The experimental results showed that the prediction model based on deep learning network and attention mechanism can well track the deterioration trend and law of CNC machine tools’ motion accuracy, and it had high prediction accuracy than traditional methods.