The practical application of shearing linear traces in a crime scene is severely restricted because of complex shapes and markedly high randomness. In this study, a more efficient matching model for clamp cutting surface traces is proposed. To this end, key theories and algorithms are further studied. The overall research approaches are as follows: the isolated forest algorithm is used for the processing of abnormal signals detected, followed by a multiscale registration framework to extract trace curve profiles, and the square speed function optimization elastic shape metric algorithm to map the profiles into an embedding. A parametric shared conjoined triple deep learning model is used, which is suitable for trace features and optimizing the triplet selection and data augmentation strategies. This system is trained by minimizing a triplet loss function, so that a similarity measure is defined by the L2 distance in this embedding. Finally, the trained model is used for similarity matching for the test set. The matching effect is continuously evaluated through experiments to provide investigators an efficient string detection method for traces.