A method for synchronizing list-mode data of single events in a PET imaging, comprising: step 1, acquiring and storing list-mode data of single events of each basic detection module working with a local reference clock source, and performing a frequency difference compensation on the list-mode data of the single events; step 2, calculating a probability density of time intervals between occurrences of single events in a single event data set acquired by each detection module, and setting respective initial parameters; step 3, performing, based on the parameters set in the step 2 and with an iterative peak searching, a synchronization correction and a coincidence peak searching on the single event data set acquired by each detection module; and determining a coarse estimated value of a detection starting time difference of each detection module; step 4, performing, based on the coarse estimated value of detection starting time difference obtained in the step 3 and with a graded time window, the synchronization correction and the coincidence peak searching on the single event data set acquired by each detection module; and determining a fine estimated value of the detection starting time difference of each detection module; and step 5, performing a weight accumulation on the coarse estimated value and the fine estimated value of the detection starting time difference, respectively obtained in the step 3 and the step 4, to determine the detection starting time difference of each detection module; and performing the synchronization correction and a coincidence discrimination on the single event data set acquired by each detection module, to obtain coincidence events. step 1, acquiring and storing list-mode data of single events of each basic detection module working with a local reference clock source, and performing a frequency difference compensation on the list-mode data of the single events; step 2, calculating a probability density of time intervals between occurrences of single events in a single event data set acquired by each detection module, and setting respective initial parameters; step 3, performing, based on the parameters set in the step 2 and with an iterative peak searching, a synchronization correction and a coincidence peak searching on the single event data set acquired by each detection module; and determining a coarse estimated value of a detection starting time difference of each detection module; step 4, performing, based on the coarse estimated value of detection starting time difference obtained in the step 3 and with a graded time window, the synchronization correction and the coincidence peak searching on the single event data set acquired by each detection module; and determining a fine estimated value of the detection starting time difference of each detection module; and step 5, performing a weight accumulation on the coarse estimated value and the fine estimated value of the detection starting time difference, respectively obtained in the step 3 and the step 4, to determine the detection starting time difference of each detection module; and performing the synchronization correction and a coincidence discrimination on the single event data set acquired by each detection module, to obtain coincidence events.