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Turnout is the key equipment for realizing the turnback of subway trains. Frequent movements and environmental changes often lead to abnormal increases in resistance during turnout conversion, resulting in jamming failures that directly affect traffic safety and efficiency. In order to effectively foresee the risk of failure during operation and minimize the adverse effects of failure, a real-time turnout jamming failure prewarning method is proposed. Firstly, a weighted grey prediction machine using PSO for weight optimization (PSO-WGPM) is proposed to predict the short-term action load index, and the predicted index series is used to characterize the future changes in the resistance state of the turnout; Secondly, the predicted index and the generated index are cascaded into a risk identification index series, and a multi-dimensional hybrid prewarning feature set with time continuity are constructed by time-domain characteristics and overload statistical characteristics of the risk identification index series; Then, the prewarning feature set is fed into the learning vector quantization(LVQ) network for prewarning discrimination, and an ensemble learning based on a hybrid voting strategy is designed to obtain the final prewarning result, in order to fit the learning environment of unbalanced small-scale samples; After giving an prewarning, the occurrence time range of jamming failure is inferred based on the overload rate of the predicted index series. The experimental results show that: the proposed method can improve the prewarning success rate and effectively control false alarms, with good correctness; it can infer the range time of fault occurrence, achieving prewarning accuracy; the prewarning calculation time is much shorter than the failure advance time, meeting the timeliness requirements for applications.