目前主流垃圾图像分类算法中存在数据集普适性差、垃圾识别种类少、分类算法局限于特定环境等问题。针对这些问题，结合垃圾图像分类的快速性与准确率的要求，提出了一种基于改进EfficientNetV2网络的垃圾图像分类算法。该算法以EfficientNetV2网络作为基准模型，通过添加SK（selective kernel）注意力机制提升分类的快速性，使用迁移学习策略提升分类的准确率。该算法利用深度学习模型框架对垃圾图像进行处理，无需对数据集图像特征进行人工提取，在实现对垃圾图像快速准确分类的同时增加了垃圾识别的种类。实验表明，新的算法在自建数据集上的准确率为99.71%，相较于GoogleNet等其他算法，提升了至少4.77%。在时间上相较于VggNet19算法等，提升了至少50%。通过改进EfficientNetV2网络，实现了更为准确快速的垃圾分类，为日益激增的垃圾问题提供了一种科学高效的解决方案。

An improved version of the EfficientNetV2 network is presented for garbage image classification to address the limitations of mainstream algorithms, such as poor dataset universality, limited recognition types, and algorithmic constraints in specific environments. The proposed algorithm emphasized both classification speed and accuracy. The EfficientNetV2 network was utilized as the baseline model, and classification speed was enhanced through the incorporation of the SK (selective kernel) attention mechanism. Transfer learning strategies were employed to improve classification accuracy. By leveraging deep learning model frameworks for garbage image processing, the need for manual feature extraction from dataset images was eliminated, and the scope of garbage recognition was expanded. Experimental results demonstrate that the proposed algorithm achieves an accuracy of 99.71% on a self-built dataset, which is an improvement of at least 4.77% compared to other algorithms, such as GoogleNet. Furthermore, in terms of time efficiency, the proposed algorithm outperforms algorithms like VggNet19 by at least 50%. Through the enhancement of the EfficientNetV2 network, accurate and faster garbage classification is enabled, providing a scientific and efficient solution to the growing challenges posed by garbage issues.