Aiming at the difficulties of manual monitoring and compliance with the current wear identification of electric power workers, the detection and identification of safety helmets, work clothes, and insulating gloves are used to carry out normative identification and warning, and a deep learning-based power worker safety wear identification method is proposed in this paper. The AlexNet and Inception are introduced to increase the width and depth of the artificial neural network. At the same time, the ReLU activation function with better performance is used to reduce the amount of network computation, and the Global Average Pooling layer is used to replace the fully connected layer with more parameters. The improved convolution neural network model has a total of 13 layers. In order to prevent the network from overfitting, the Early-stopping mechanism and the L2 regularization method are used to improve the performance of the network model. The experimental results show that the algorithm can achieve a good recognition effect on the staff who do not wear safety according to the regulations in the video, and the feasibility and effectiveness of the algorithm in practical application are verified.