The rail vehicle door system is one of the key components of rail vehicles. Its failure rate accounts for more than 30% of vehicle failures. By analyzing early warnings provided by subhealth data from the door system, the efficiency and reliability of their health maintenance can be effectively improved and stable operation of the door system can also be guaranteed. In this paper, early-stage resistance changes in the subhealth state of rail vehicle door systems are considered as the research object. Firstly, the distribution rules for the motor parameters are studied, and the time-domain and normal operating envelope features of the operating motor are extracted. Secondly, subhealth conditions with different resistances are simulated using a test rig, and the experimental data are applied to summarize the rules. According to the subhealth types and the distribution of features, diagnostic rules for subhealth are formulated. To check the possibility of fault diagnosis, a verification using running rail vehicle door system data is carried out in MATLAB. The results reveal that the misdiagnosis rate of resistance subhealth is 0% while the rate of missed diagnoses is 2%. Meanwhile, the diagnostic process based on the established rules is relatively efficient. This method is suitable for application for resistance subhealth diagnosis of urban rail vehicle door systems.