Rock thin section recognition provides geological information, which is crucial in petroleum geology, exploration, and mining research as a kind of fundamental work. Although many machine learning methods solve this research, there are still problems of data hierarchy and model pertinence. We use the hierarchical classification method to divide the dataset into sedimentary, metamorphic, and igneous rock as first-level, and to subdivide a total of 105 s-level further from the three categories. We propose the MaSE-ResNeXt model based on the fundamental work in the SeNet that can enhance the feature connection between different channels. The MaSE-ResNeXt adopted hierarchical filter groups, bottleneck stacking, and other strategies to enhance the representational capability of the model which advances the solving ability in rock recognition. Six data enhancement methods and other techniques are used to improve the robustness and effectiveness. The accuracy in the test set was 90.89% and 81.97% for the first and second level, respectively, with the inference duration is only 0.0357s. This study also designs a degeneration experiment and model comparison to demonstrate the model's effectiveness. Future research can employ this model as the fundamental base of transfer learning in geology to save time in the training of study.

岩石薄层识别提供地质信息，这对石油地质，勘探和采矿研究至关重要，是一种基础工作。尽管许多机器学习方法解决了这项研究，但仍然存在数据层次结构和模型相关性的问题。我们使用分层分类方法将数据集分为沉积岩，变质岩和火成岩作为第一级，并从这三类中进一步细分总计105 s级。我们基于SeNet的基础工作提出了MaSE-ResNeXt模型，该模型可以增强不同渠道之间的功能联系。MaSE-ResNeXt采用了分层过滤器组，瓶颈堆叠和其他策略来增强模型的表示能力，从而提高了岩石识别的求解能力。使用六种数据增强方法和其他技术来提高鲁棒性和有效性。测试集第一级和第二级的准确度分别为90.89％和81.97％，推断持续时间仅为0.0357s。这项研究还设计了变性实验和模型比较，以证明模型的有效性。未来的研究可以将该模型用作地质学中转移学习的基础，以节省研究培训的时间。