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Abstract: The extensive use of steel spring floating slab tracks has effectively addressed the challenge of alleviating the environmental vibrations induced by urban rail transit systems. However, under the combined action of train dynamic loads and complex environmental factors, problems, such as the fracture of steel spring vibration isolators and suspension vibrations induced by the uneven settlement of the base, often occur. The failure of isolator support stiffness is often hidden in its early stages and is challenging to identify by conventional detection methods. At the same time, it will aggravate the wheel-rail interaction, accelerate the deterioration of track structure, and can even affect the driving safety. This study first establishes a detailed coupled train–floating slab track–foundation analytical model. Then the influence of the vibration isolator support stiffness failure on the dynamic indices of the floating slab track system response is analyzed. A set of defect identification methods that can detect the number of failed steel springs, severity of damage and its location are proposed. Finally, an intelligent monitoring system for support stiffness of floating slab track is built by combining the density-based spatial clustering of applications with noise algorithm and statistical data analysis and is applied to a rail line in southern China. During a three-year monitoring campaign, a suspension failure and a fracture of a steel spring were each successfully detected and detailed failure information was obtained. Field investigation results were consistent with the damage identification results. After repair, the track structure dynamic response returned to the average pre-damage level and further deterioration had been arrested. The proposed damage identification methods and monitoring system provide an approach for intelligent identification of track structure support stiffness failures.