For the problem of high complexity of high-voltage cable faults and high cost of real-time monitoring, a combined diagnosis method based on ensemble empirical modal decomposition (EEMD) and tennies whisker search algorithm optimized convolutional neural network (CNN) is proposed. The cable sheath current data are decomposed into several eigenmodal components (IMF) by EEMD, and the component with the highest correlation with the original signal is selected by combining the correlation coefficients as the input of the CNN network. In order to improve the classification accuracy of the network model, the hyperparameters of the CNN diagnostic model are optimized using the aspen whisker algorithm (BAS). Taking the high-voltage cable current data of a coal mine in Huainan as an example, the experimental results show that EEMD effectively decomposes the current signal, and the designed BAS-CNN network has the highest classification accuracy with 96.95% monitoring accuracy compared with 2 groups of networks with artificially determined CNN hyperparameters.