This study proposes a multi-scale key point extraction algorithm based on normal weighting to address the sensitivity to noise and dependency on object models' shape features in traditional keypoint detection algorithms. First, at each scale, the covariance matrix of the local neighborhood is established and the ratio of the local coordinate system appearing on the first two axes is calculated. Thus, candidate keypoints are determined based on the ratio. Then, to measure the local maximum dissimilarity measured value of the point cloud, the normal weighted shape index value is calculated. Finally, the maximum value point of the local maximum dissimilarity measured value at different scales is selected as the final keypoint. The experimental results show that compared with other traditional algorithms, the proposed algorithm can effectively extract keypoints of various point cloud models and simultaneously consider the quality and quantity of keypoints and operating efficiency. Moreover, the proposed algorithm has strong adaptability for models with sharp features and large area smooth features, which enhances its robustness and shape index function.