To address the problems associated with the large amounts of data and high redundancy of three-dimensional point cloud related to complex object surfaces obtained by a laser line structured light scanner, a point cloud simplification algorithm based on self-adaptive neighborhood and local contribution value is proposed. First, according to the local geometric characteristics of the point cloud, the best neighborhood range is selected. Then, the best neighborhood, internal shape feature algorithm, and local surface patch algorithm are combined to calculate the local contribution values of all point cloud data and the feature points of the point cloud are extracted. Finally, K-means clustering algorithm is used for classification and the point cloud is simplified based on the classification results and the contribution values. The experimental results show that for complex surface test objects, the proposed algorithm can adjust the simplification of characteristic and noncharacteristic areas while ensuring the simplification rate as well as the overall integrity and detailed feature information of the point cloud. Consequently, the simplification result has higher accuracy and fits the original appearance of the object more closely.