Non-line-of-sight (NLOS) errors significantly impact the accuracy of ultra-wideband (UWB) indoor positioning, posing a major barrier to its advancement. This study addresses the challenge of effectively distinguishing line-of-sight (LOS) from NLOS signals to enhance UWB positioning accuracy. Unlike existing research that focuses on optimizing deep learning network structures, our approach emphasizes the optimization of model parameters. We introduce a chaotic map for the initialization of the population and integrate a subtraction-average-based optimizer with a dynamic exploration probability to enhance the Snake Search Algorithm (SSA). This improved SSA optimizes the initial weights and thresholds of backpropagation (BP) neural networks for signal classification. Comparative evaluations with BP, Particle Swarm Optimizer–BP (PSO-BP), and Snake Optimizer–PB (SO-BP) models—performed using three performance metrics—demonstrate that our LTSSO-BP model achieves superior stability and accuracy, with classification accuracy, recall, and F1 score values of 90%, 91.41%, and 90.25%, respectively.