[Objective] To address the difficulty in heat dissipation of drive motors used in vacuum pumps during actual operation and the tendency of its temperature to exceed insulation limits under impact loads, a neural network method is used to predict the heating of vacuum pump drive motors. [Methods] Taking a 4.5 kW vacuum pump drive motor as an example, the temperature simulation analysis of the motor under impact loads was first conducted. The instantaneous data were classified, and a neural network was trained using historical data to establish the mapping relationship between the operating data and temperature of the vacuum pump drive motor. Subsequently, a motor test platform was constructed to conduct impact tests on the motor. The test data were used to correct the neural network training model. By comparing predicted temperatures with actual measured temperatures through experiments, the accuracy of the corrected model was validated. [Results] The experimental results showed that the accuracy of the neural network model for temperature prediction was improved after error correction. The bidirectional long short-term memory network with error correction outperformed the convolutional neural network and the long short-term memory network in predicting the temperature at the stator winding end, with a coefficient of determination reaching 0.979. [Conclusion] This study provides a method and a technical approach for heating prediction of vacuum pump drive motors under impact loads without relying on temperature sensors, offering an algorithmic basis for the effective control of drive motors.