Abstract: A novel attention-enhanced topology and convolution model,which combines persistent homology and convolutional neural network through an attention-guided fusion framework,is proposed to address degradation in classification performance caused by insufficient topological feature extraction and weak intra-class structural consistency.Persistent homology captures key topological structures and encodes them into feature descriptors,while convolutional neural network extracts local visual features through convolution and pooling operations.An attention mechanism then merges both into a unified global representation to enhance feature completeness.The Wasserstein distance cross entropy loss function,derived by integrating the measurable Wasserstein distance with the cross-entropy loss,is used to constrain the topological structures of images.This effectively mitigates inter-class topological ambiguity,thereby enhancing the classification performance,robustness,and accuracy of the AETC model.Models with various topological vectorization methods are evaluated on three diverse datasets,the AETC model improves ACC by 2%~11%,AUC by 1%~7%,F1-score by 1%~11%,and mAP by 3%~17%.Within the classical convolutional neural network framework enhanced by persistence landscape vectorization,the optimal model achieves peak ACC of 95.49%,AUC of 99.44%,F1-score of 95.48%,and mAP of 98.42%.

Key words: Persistent homology, Topological features vectorization, Image feature fusion, Attention mechanism, Convolutional neural network, Image classification