Sea state bias is a key source of error in satellite altimetry. Even after good correction algorithms are applied, residual errors of a few millimeters to a few centimeters may still remain, which is not negligible for satellite altimeters with centimeter-level accuracy. In the era of SWOT, accurate sea state bias correction remains a major challenge for this interferometric imaging altimeter. The current SWOT mission mainly relies on a pretrained low-dimensional nonparametric model that estimates sea state bias from significant wave height and wind speed, a strategy that has long been used for conventional nadir-looking altimeters. However, SWOT is designed for wide, two-sided swath observations spanning hundreds of kilometers, and the signal is not perpendicular to the sea surface waves. These viewing-geometry factors change the magnitude of sea state bias, so conventional nonparametric models do not correct SWOT sea state bias completely. Analysis shows that after the default sea state bias correction in SWOT data products, an additional residual error related to cross-track distance appears, and the residuals are also correlated with mean wave period, relative wave angle, and other parameters. To address this limitation, we propose a sea state bias estimation model based on a Siamese neural network architecture. The model takes short-term paired ascending and descending observations from self-crossovers as input, including sea surface height anomaly, significant wave height, wind speed, backscatter coefficient, mean wave period, and viewing-geometry parameters. Through the Siamese architecture, two multilayer neural networks with shared weights are combined, and a loss function is constructed from initial guess labels, self-crossover sea surface height mismatch values, and knowledge labels, so that the model learns globally optimal parameters from paired data and avoids local distortion. Preliminary experiments show that the proposed model reduces the crossover residual variance on the validation set from 33.96 cm² to 20.50 cm² and the root mean square error (RMSE) from 5.83 cm to 4.53 cm, corresponding to a 38.93% improvement over the NPSSB correction model used in SWOT data products. These results indicate that this Siamese learning framework, which incorporates viewing-geometry information, can effectively improve sea state bias correction and provides a promising correction method for improving the quality of wide-swath altimeter data products.