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Improve the intelligent management level of subway track, a new intelligent subway track management system was designed. Naive Bayes Classifier, Logistic Regression Classifier and Support Vector Machine Classifier were designed respectively, and a track state prediction model based on Stacking ensemble algorithm was constructed. Using the equipment data, inspection data and maintenance data of XX Metro Line 1, 2 and 6 from 2015 to 2021, the validity of the model was verified. Furthermore, an Adaptive Learning Markov Decision Process (AL-MDP) was introduced, and an optimization model of track maintenance decision based on Stacking-SVM was constructed. The model was used to sense the running state of the subway track and predict the abnormal state, and continuously learned the deterioration mechanism of the subway track through the process of adaptive learning, and provided personalized and refined decision-making suggestions for the state warning and maintenance strategy of the track. Finally, the intelligent subway track management system was designed and implemented. After the introduction of AL-MDP, the maintenance cost of the subway track was further reduced, and the operation status of the subway track can be grasped in real time. This research provides managers and workers with refined, personalized and more scientific maintenance optimization decisions, and achieves dual precise control of maintenance costs and track safety.