岩石断口微观形貌识别对于揭示岩石断裂失效机理具有关键作用。深度学习在图像识别领域有显著成效，但其在岩石断口图像识别上仍面临主观性强和特征复杂等问题。为了提高岩石断口图像识别的准确性，提出一种改进的InceptionV3模型，该模型融合了Self-Attention与双线性注意力机制，对局部和全局特征提取，结合特征拼接融合技术，提升模型有效特征的提取能力，实现岩石断口图像识别。以岩石断口数据集为例进行分析，结果表明：改进的InceptionV3模型在砂岩断口图像识别方面准确率达到98.27%，使用验证集对训练好的模型进行验证，Precision、Recall和F1-Score均达到85%，验证了模型的高效性和可靠性。研究成果为岩石断口图像的自动化、高精度识别提供了一种新的解决方案，有利于岩石断裂失效的判定，对岩体工程灾害的预防具有重要的指导意义。

The microscopic morphology recognition of rock fractures plays a crucial role in revealing the fracture failure mechanism of rocks. Deep learning has shown significant success in image recognition, but it still faces issues such as strong subjectivity and complex features in rock fracture image recognition. To improve the accuracy of rock fracture image recognition, an improved InceptionV3 model was proposed. This model integrated Self-Attention and Bilinear Attention Mechanism to extract both local and global features, and combined feature concatenation fusion technology to enhance the model's ability to extract effective features, enabling rock fracture image recognition. The rock fracture dataset was used for analysis. The results show that the improved InceptionV3 model achieves an accuracy of 98.27% in sandstone fracture image recognition. The model is validated using a validation set, and the Precision, Recall, and F1-Score all reach 85%, confirming the model's efficiency and reliability. The research results provide a new solution for the automated and high-precision recognition of rock fracture images, which is beneficial for determining rock fracture failure and has important guiding significance for the prevention of geological engineering disasters.