Abstract The insulation system of the mine motor is key to ensuring its stable operation, which affects the efficiency of mine production and transportation and the benefit of enterprises. The prediction of motor insulation life can effectively prevent the deterioration of motor performance. Due to the complex underground environment, many factors affect the insulation of coal mine motors, which are not conducive to the selection of feature parameters. The insulation life prediction efficiency and accuracy of motors need to be improved. To solve this problem, this paper proposes a prediction method based on Conv1d (one-dimensional convolve) and LSTM (long short-term memory neural network), which integrates multiple models and the Stacking integrated algorithms. The operating data and environmental parameters of a coal mine motor under different conditions are analyzed. Three Conv1d-LSTM hybrid models with batch normalization, residual network, and dense number are used as the base learner, and the results are weighted by K-fold to verify the accuracy of the three models. The linear regression model is used as a meta-learner to prevent overfitting of the model and effectively improve the speed and accuracy of single network optimization. Compared with the traditional method, the results show that the optimization of the LSTM neural network by the multi-feature model has a higher accuracy in predicting the remaining life of motor insulation, which provides a new way for the prediction of motor insulation life.