The cage netting is prone to breakage and once it is not repaired in time, it can cause huge economic losses to farmers. In order to realize the intelligent damage detection of cage netting, this study proposes a damage identification method based on improved YOLOv7.We use the gnConv structure in the backbone network and the SimAM module in the neck network to improve the model's expressiveness and better focus on the features at the cage netting breakage. So that the detection precision of the model is improved. Using depthwise separable convolution in Backbone networks with reduced activation functions and varying convolution step sizes. The model is also reconstructed in the neck network using the Bottleneck module with a 1×1 convolution kernel and the Mish activation function with better performance. As a result, the number of parameters and the cost of operations are reduced, resulting in an increase in the speed of model inspection and a reduction in size. The results of ablation tests and comparative tests showed that the average precision of YOLOv7-C3NeHX algorithm is 3.1 percentage points higher than of the original YOLOv7 algorithm, and its precision, recall and F1 score are 0.5, 4.2 and 3 percentage points higher, respectively. Detection speed up to 232.56 FPS. GFLOPs and model size account for 38.2% and 94.3% of the original YOLOv7. The improved model can effectively improve the identification efficiency and deployment flexibility, and provide technical support for the research and development of intelligent clothing repair robot.