As an innovative form of rail transportation, maglev trains have attracted considerable attention regarding their operational safety and stability, rendering the performance evaluation of their suspension control systems particularly crucial. This paper develops a comprehensive data-driven methodology for assessing the performance of maglev suspension control systems, constructing a multi-dimensional evaluation framework that incorporates stochastic, deterministic, and τ-distance metrics. Through the establishment of AutoRegressive Moving Average (ARMA) time series models, this research systematically extracts four categories of performance indicators: step response parameters, error integral criteria, minimum variance indices, and passenger comfort metrics. The proposed methodology’s feasibility and practical effectiveness are rigorously validated using operational data obtained from an actual maglev test line, thereby successfully bridging the gap between theoretical performance assessment and engineering implementation in high-speed maglev transportation systems.