To enhance the stability of angle measurement methods based on single-vision techniques, which are susceptible to random disturbances from environmental or systemic sources, we propose a dual-viewpoint visual angle measurement method based on complex-valued neural networks. Feature extraction is conducted manually, followed by an assessment of the features’ relevance and monotonicity with respect to angles to facilitate feature selection. To address the significant numerical discrepancy between the 0° and 360° labels, which impacts training outcomes, angles are represented using Euler′s formula. This representation facilitates the construction of a complex-valued neural network with both complex inputs and outputs for angle computation. Experimental results demonstrate a significant improvement in measurement accuracy; the proposed method reduces the mean error by 0.322° and the root mean square error by 0.64° compared to methods based on deep neural networks using a single viewpoint, maintaining high performance across various environmental test sets. By leveraging the robustness against environmental disturbances provided by dual viewpoints and the strong fitting capabilities of complex-valued neural networks for angle labels, this model enhances the accuracy and stability of radial visual angle measurements while adhering to the constraints and stability of mathematical models.