A lot of line traces on the bearing surface of broken ends which usually present nonlinear morphological features and have strong randomness were left in the crime scene of cable cutting case. In order to implement trace feature matching and affiliated tool inference more rapidly, an efficient matching technique for laser detection features of cable cutting traces was designed: K-Means clustering was used to implement abnormal data correction for 1-D signals picked up on the surface of broken ends detected by single-point laser displacement sensor firstly, and then self-adaptation correction of rotation angle was implemented to unify matching datum. Finally, matching strategy based on threshold sequences was used to realize overlap ratio matching of trace feature similarity, thus realizing quick inference of corresponding tools, and cutting tool interference experiment by actual traces verifies practicability and effectiveness of the technique. © 2017, Science Press. All right reserved.