Wildlife plays a pivotal role in biodiversity conservation, and field resource surveys form the fundamental basis. Efficient recognition of monitoring images is a critical prerequisite for scientific wildlife resource investigation and protection. However, domain shift issues caused by varying lighting conditions, backgrounds, shooting scales, and species differences often degrade recognition model performance. This study aims to enhance the generalization capability of wildlife species recognition under complex unlabeled field environments, providing key technical support for open-environment wildlife classification research.

This paper proposed a deep convolutional domain adaptation model for wildlife image recognition to enhance cross-domain accuracy in unannotated scenarios. Specifically, the model employs a pre-trained ResNet50 network as a feature extractor to capture domain-invariant convolutional features of wildlife images through maximum mean discrepancy (MMD) constraints. A mixup-based probabilistic distribution feature alignment module is designed to map semantic spaces in fully connected layers, enhancing the model's ability to learn high-level semantic information. Finally, entropy regularization constraints and target domain correlation mining are integrated to optimize low-density separation boundaries between categories, further improving the model's generalization and robustness.

A series of experiments were performed on two wildlife datasets, which contain 8 and 11 species respectively, to validate the model's effectiveness. The results demonstrate that the proposed model achieves average accuracies of 98.3% and 81.7% on the two datasets, significantly outperforming adversarial learning-based baseline models in wildlife image recognition.

The proposed deep convolutional domain adaptation-integrated wildlife image recognition model effectively mitigates domain shifts caused by spatiotemporal scenario variations and species differences, thereby enhancing cross-domain wildlife species recognition accuracy. This approach offers a robust technical framework for advancing wildlife conservation and ecological monitoring.