A multi-layer coupling relationship-based recognition method for train operation time deviation propagation condition, train operation including a multiplicity of train activities, each formed by two associated train events with passing a plurality of stations in an event time sequence comprising arrival interval activities, stop activities, departure interval activities, and turn-back activities at the stations, and section operation activities between two stations, wherein each arrival interval activity defines an arrival event and each departure interval activity defines a departure event at the respective station, the recognition method comprising the following steps: recognizing an effective train arrival event and departure event time sequence, comprising a respective arrival event and a respective departure event of a train at each passed station; uniformly extracting train activity data, comprising any stop activity, any section operation activity, any turn-back activity, any arrival interval activity, and any departure interval activity; constructing coupling relationship groups of train operation time deviations in the effective train arrival event and departure event time sequence between any train event and any train activity and between train activities, the coupling relationship groups comprising: - a coupling relationship group between any arrival event at a station and an activity associated with the arrival event, - a coupling relationship group between any departure event and an activity associated with the departure event, - a coupling relationship group between adjacent activities of the same train; and - a coupling relationship group between adjacent activities of adjacent trains; and performing statistics on changes of train operation time deviations in each relationship group, the changes of train operation time deviations in each relationship group including the relationship between any activity and any event, statistically fitting a distribution function of activity time deviations changing with event time deviations, and for the relationship groups between any activities, counting a degree of change for train operation time deviations of each relationship group of associated activities in each time period and each line section, and outputting a respective distribution function and a time-space distribution visualized result. recognizing an effective train arrival event and departure event time sequence, comprising a respective arrival event and a respective departure event of a train at each passed station; uniformly extracting train activity data, comprising any stop activity, any section operation activity, any turn-back activity, any arrival interval activity, and any departure interval activity; constructing coupling relationship groups of train operation time deviations in the effective train arrival event and departure event time sequence between any train event and any train activity and between train activities, the coupling relationship groups comprising: - a coupling relationship group between any arrival event at a station and an activity associated with the arrival event, - a coupling relationship group between any departure event and an activity associated with the departure event, - a coupling relationship group between adjacent activities of the same train; and - a coupling relationship group between adjacent activities of adjacent trains; and - a coupling relationship group between any arrival event at a station and an activity associated with the arrival event, - a coupling relationship group between any departure event and an activity associated with the departure event, - a coupling relationship group between adjacent activities of the same train; and - a coupling relationship group between adjacent activities of adjacent trains; and performing statistics on changes of train operation time deviations in each relationship group, the changes of train operation time deviations in each relationship group including the relationship between any activity and any event, statistically fitting a distribution function of activity time deviations changing with event time deviations, and for the relationship groups between any activities, counting a degree of change for train operation time deviations of each relationship group of associated activities in each time period and each line section, and outputting a respective distribution function and a time-space distribution visualized result.