The multi-source monitoring data of switchgear contains rich equipment operating status information，and analyzing it can achieve switchgear fault diagnosis. A fault diagnosis method for switchgear based on SMOTE-SSA-CNN was proposed. Firstly，based on monitoring data such as switchgear voltage，current，and temperature and humidity，the synthetic minority over-sampling technique（SMOTE） algorithm was used to expand the original dataset，solving the problem of severe imbalance between positive and negative samples in the original dataset. Then，the sparrow search algorithm（SSA） was introduced to optimize the hyperparameters of convolutional neural networks（CNN），such as the size and number of convolutional kernels，the number of fully connected layer neurons，and the learning rate，in order to improve the accuracy of the model's fault diagnosis results. Finally，the performance of the established SMOTE-SSA-CNN model was evaluated through example analysis，verifying the effectiveness of the proposed method for switchgear fault diagnosis. Compared with traditional fault diagnosis methods，the proposed method has better convergence and higher accuracy.

switchgear  /  multi source monitoring data  /  synthetic minority over-sampling technique（SMOTE） algorithm  /  sparrow search algorithm（SSA）  /  convolutional neural network（CNN）