为了改善由于地质数据类别不均衡导致的岩性预测精度不高的问题，提出了一种ECA-MSCB ResNet模型，集成高效通道注意力机制（efficient channel attention, ECA）和多尺度卷积块（multi-scale convolutional block, MSCB）于传统的ResNet架构中，实现了对岩性数据特征的高效提取和表征。针对岩性类别不均衡的问题，在模型训练过程中引入先验概率平衡logit偏差，改进焦点损失函数，以提升对少数类岩性的识别能力。实验结果表明，基于ECA-MSCB ResNet的模型在地质岩性不均衡数据集上表现良好，与原ResNet模型相比，平均预测准确率提升约7.45%，与随机森林相比提升27.33%，特别是在少数类岩性的识别上取得了显著进步，平均提高约17.9%。同时，本文模型在公开数据集上表现良好，F₁-score达到75.77%。此外，本文模型识别准确率高于目前主流方法，在地质不均衡岩性识别领域具有良好的应用价值。

In order to improve the prediction accuracy of lithology affected by imbalanced geological data, an ECA-MSCB ResNet model was proposed. The model integrates ECA (efficient channel attention) and MSCB (multi-scale convolutional block) into the traditional ResNet architecture to achieve efficient extraction and representation of lithological data features. For the issue of imbalanced lithology categories, prior probability-balanced logit bias was introduced during model training, and the focal loss function was modified to enhance the recognition of minority lithology classes. Experimental results show that the model based on ECA-MSCB ResNet performs well on the imbalanced geological lithology dataset, achieving an average prediction accuracy improvement of approximately 7.45% compared to the original ResNet model and 27.33% compared to the random forest method. Notably, the recognition of minority lithology classes improves by an average of 17.9%. Furthermore, the model demonstrates strong lithology classification ability on public datasets, achieving an F₁-score of 75.77%. In addition, the recognition accuracy of the proposed model outperformed both traditional and mainstream methods. The ECA-MSCB ResNet method holds significant application value in the field of imbalanced geological lithology recognition.