Aiming at the problems of limited image scale and low recognition accuracy in the field of substation equipment inspection image recognition, an image classification and recognition method based on improved ResNet34 network is proposed. The Seam Carving algorithm is employed to compress the lowenergy areas in the image for the preservation of key features. Additionally, six types of image enhancement techniques such as elastic transformation and Gaussian noise are utilized to increase the diversity of the images. The basic ResNet34 network is integrated with the convolutional block attention module to enhance the model′s ability to extract key features from equipment inspection images. A model pre-trained on the ImageNet dataset is utilized as a feature extractor for transfer learning to address the issue of insufficient sample quantity. A cosine annealing strategy is introduced in the Adam optimizer to dynamically adjust the learning rate, to make the improved ResNet34 network converge to the optimal solution faster. Experimental results show that the proposed method improves accuracy by 0.073 3 and reduces the loss rate by 0.201 9 compared to the basic ResNet34 network, which provides a reliable solution for the field of substation equipment inspection image recognition.