To address the poor robustness, low accuracy, and slow operation of existing color-ring resistor recognition methods, this study proposes a lightweight image recognition algorithm for color-ring resistors based on the MobileNetV3 network. Firstly, data augmentation is conducted on a self-built data set to increase the sample size and improve the robustness of the model. Secondly, a convolutional?block?attention?module (CBAM) attention module is utilized in the bottleneck structure, which can enhance the ability of the model to refine features in space and channels for accuracy improvement. Thirdly, the classifier is optimized by removing the redundant dimension-increasing operations, which can reduce the number of parameters while improving accuracy and thereby speed up the operation of the model. Finally, a skip connection is embedded in the network in response to unequal feature image sizes and channel numbers. This makes the model able to extract more features from deep networks and improves accuracy. The experimental results show that the model can recognize color-ring resistors quickly and accurately, with its accuracy reaching 98% on the self-built data set. The model can provide a new technical reference for the automatic recognition of resistors.