To address the low accuracy of the single-geometric-feature-based matching method for cultural relic fragments， an alternative multi-feature-parameter-fusion-based automatic matching method is proposed herein. For this， first， a segmentation algorithm is used to extract the fracture surfaces of cultural relic fragments， and the following four characteristic parameters of points on these fracture surfaces are computed： the average distance from a point to its neighborhood points， distance from a point to the gravity center of its neighborhood， curvature， and average value of the normal included angle of the neighborhood. Following this， the four feature parameters are fused to obtain feature discrimination parameters， and a feature point set is extracted by judging the value of these feature discrimination parameters. Finally， the iterative closest point algorithm based on the scale factor is used to match the feature point set， and consequently， accurate fracture surface matching of cultural relic fragments is achieved. In the experiment， a point cloud data model of Terracotta Warriors fragments is used to verify the performance of the multi-feature-parameter-fusion-based matching method for cultural relic fragments. The results reveal that the proposed matching method can overcome the low accuracy of the single-geometric-feature-based matching method. Compared with the matching accuracy of the existing algorithm， that of the proposed algorithm is improved by more than 15%， while its time efficiency is improved by more than 20%. Therefore， the multi-feature-parameter-fusion-based matching method is effective for cultural relic fragment matching.