To address the braking modeling challenges in medium-low speed maglev trains caused by electric braking response delays and discrete hydraulic compensation in conventional single-mass models, a hybrid TCN-Transformer architecture-based braking model was proposed. A three-stage preprocessing framework was developed, comprising: eddy-current speed measurement data imputation, operational state decomposition, and multi-scale window feature generation. The local temporal pattern extraction capability of TCN (temporal convolutional networks) was integrated with the global dynamic correlation modeling advantages of Transformer, establishing a predictive framework for maglev train braking characteristics. Experimental results demonstrated that MAE (mean absolute error) of 1.114 km/h for 50-step prediction was achieved, which represented a 7.2% improvement over standalone Transformer models. Field validation using in-service track measurement datasets demonstrated that the braking response time was 22.1 s earlier than that of conventional dynamic models, overspeed fluctuations (>0.5 km/h) in maximum speed-restricted segments were completely eliminated, and the terminal positioning error was reduced by 32.4%. It was demonstrated that the hybrid architecture effectively resolved nonlinear modeling challenges in electro-hydraulic hybrid braking dynamic compensation through multi-scale feature fusion, providing a real-time predictive solution for intelligent braking systems in maglev transportation.