Abstract: To solve the complex background interference problem of existing convolutional neural networks in soybean leaf disease classification, an improved ConvNeXt algorithm is proposed, and two common diseases of soybean as well as healthy leaves are classified and identified. By adding multiple attention modules to the traditional ConvNeXt algorithm, the network is made more capable of focusing on discriminative features and the LeakyReLu activation function is chosen instead of the ReLu activation function to avoid the neuron deactivation phenomenon. In addition, the robustness of the network was improved by performing data enhancement on the dataset to diversify the disease dataset. The results showed that the improved ConvNeXt algorithm outperforms the original ConvNeXt, ResNet50 and Swin Transformer, all three comparison models, in terms of average classification accuracy on the test set. The average recognition accuracy on the data enhanced test set reached 85.42%, and the research results can provide reference for solving the classification of soybean leaf disease images under complex background information interference.