[Objective] A method has been proposed to preprocess, predict, and classify the load data of automated guided vehicle (AGV) drive motors, and then predict the health status and failure of AGVs. The aim is to evaluate the health status and failure probability of AGV motors, and improve the maintenance and work strategies of AGVs. [Methods] Based on the experimental collection of AGV motor channel current, vibration signal data, and temperature data, the data is sampled. The proposed method uses autoregressive model and convolutional neural network model to predict the health status and calculate the failure probability of AGV drive motor's current, vibration signal, and temperature rise data trends. The collected and predicted data are converted into symmetrized dot pattern (SDP) images using SDP algorithm for classification detection, thereby determining the health level of the working motor. After classifying the health level of the dataset into three levels based on the temperature rise data of the motor, the autoregressive model and Convolutional network model is used to detect the health status of the driving motor and estimate the probability of the driving motor health level based on the load current and vibration signals of the AGV motor. Based on the determination of the health status, the statistical model can calculate the failure probability of the AGV driving motor. [Results] The data verification through the acceleration test of the driving motor shows that the accuracy of this method in evaluating the health status diagnosis of AGV driving motors reaches an average of about 99.7%, with an accuracy of 100% in classifying the test samples as healthy and unhealthy. When predicting the probability of AGV drive motor failure under planned workload, the root mean square error of AGV motor state data prediction reaches around 0.053. [Conclusion] By applying deep learning methods to the current, vibration, temperature rise and other data of AGV motors, the health level classification of AGV motors (healthy, sub healthy, unhealthy) is achieved. Based on the evaluation results of health status and the calculation of failure probability, reference is provided for the assessment of AGV workload intensity and maintenance plan.