Abstract: To address the challenges in deep learning-based underwater acoustic source range estimation caused by high-dimensional discrete labels and scarce intra-class samples that constrain model feature learning, this study proposes a siamese neural network-based feature extraction method for target range estimation. First, a dataset containing distance-labeled positive/negative sample pairs was constructed using simulated data, followed by the design and training of a siamese neural network to extract range-discriminative features. Subsequently, siamese neural network feature extraction-based convolutional neural network (S-CNN) and siamese neural network feature extraction-based residual neural network (S-ResNet) were developed through transfer learning strategy. Simulation results demonstrate the method’s effectiveness in enhancing range-sensitive feature representation: S-CNN/S-ResNet outperformed baseline models without feature extraction (NS-CNN/NS-ResNet), with S-ResNet exhibiting superior robustness across varying training sample sizes, signal-to-noise ratios, and environmental uncertainties. The SWellEX-96 experiment validation confirmed the significant advantages of the proposed method over conventional matched-field localization techniques. Notably, the S-ResNet achieved 10% higher confidence probability and 2% lower mean percentage error compared to S-CNN.