Garbage classification, as one of the important links of resource recycling, can effectively improve the efficiency of resource recycling and further reduce the harm caused by environmental pollution. With the development of modern industry, traditional image classification algorithm cannot meet the requirements of garbage sorting equipment. This study proposes a garbage classification model based on convolutional neural networks (Garbage Classification Network, GCNet). By constructing the attention mechanism, the model completes extracting the local and global features and can obtain perfect and effective feature information. At the same time, the feature fusion mechanism is used to fuse features at different levels and sizes, which can effectively use features and prevent gradient from vanishing. The experimental results prove that GCNet has achieved excellent results on garbage classification datasets and can effectively improve the accuracy of garbage classification.