The invention discloses a detection method for an abnormal behavior of a vehicle based on spectrum clustering. The detection method comprises the following steps: obtaining a space-time track of a moving target through video tracking; removing abnormality and preprocessing, thereby obtaining a normal track; constructing an image for the track, thereby obtaining an undirected image corresponding to a track sequence; calculating similarity among tracks, thereby obtaining a similarity matrix; performing Laplace transformation on the similarity matrix, thereby obtaining a Laplace matrix; clustering the feature vector matrix of the front k maximal feature values; after performing mode learning on a motion track, obtaining motion modes of the target under a normal state; if a new track meets one of the motion modes, i.e. a normal motion mode, confirming that the traffic is normal; and if not, confirming that the vehicle abnormally runs, namely, the traffic abnormality occurs. According to the detection method, through the clustering learning for the vehicle track, the monitoring for the abnormal behavior of the vehicle is realized, the abnormal lane change is detected and the basis for automation of traffic management is supplied.