Abstract: Conodonts are internal shell fossils of ancient marine organisms， and their accurate identification is vital for understanding Earth’s climatic history and geological transitions. Traditional image recognition techniques primarily focus on broad category classification， which fails to meet the complex demands of fine-grained conodont classification and struggles to capture subtle yet critical feature differences. We propose a bilinear convolutional neural network（BCNN） enhanced with feature recalibration mechanisms. By integrating squeeze-and-excitation（SE） attention mechanisms and residual connections， the model’s feature extraction capability is significantly enhanced. The SE module recalibrates features by modeling inter-channel dependencies， while residual connections mitigate the vanishing gradient problem using skip connection， ensuring efficient feature transmission and reusing in deeper layers. Experimental results on a fine-grained conodont dataset demonstrate that the SE-BCNN outperforms existing methods in accuracy， precision， recall， and F1 score， achieving a classification accuracy of 89%， significantly surpassing models such as VGG16， ResNet18-BCNN， and CART.

Key words: conodont fossils, fine-grained recognition, feature recalibration, bilinear convolutional neural network, residual connections