Crop diseases and pests are major threats to crop yield and quality, as well as global food security and agricultural livelihoods. Therefore, it is essential to identify crop pests and diseases in a timely, efficient and accurate manner. In this paper, we propose MConvNeXt, a crop pest classification model based on multi-scale feature fusion, which adds a multi-scale module to the original ConvNeXt network model to better capture feature information at different scales. We experimentally analyzed the MConvNeXt model on the IP102 dataset, and the results show that it outperforms the baseline network and other classical image classification models in pest classification.