The retrieval methods based-on locality-sensitive hashing (LSH) provide a feasible solution to the problem of approximate nearest neighbor (ANN) search on high-dimensional, multiple distributed characteristics, and massive data. However, there are still some unresolved problems in open environment, such as poor adaptability to the data with multiple distribution characteristics. Based on the fact that Laplacian operator is sensitive to sharp changes in data, an LSH retrieval method based on Laplacian operator (LPLSH) is proposed, which is suitable for data in open environment with a variety of distributed characteristics, and can segment data on global view. By applying Laplacian operator to the probability density distribution of data projection, the position of the sharp change of distribution will found as the offset of the hyperplane. This study proves theoretically that the reduced dimension can keep the local sensitivity characteristics of the hash function, and the global low projection density interval segmentation is helpful to improve the precision. The guiding significance of using Laplacian operator to obtain the second derivative to set the hyperplane offset is also analyzed. Compared with the other 8 methods based on LSH, the F1 value of LPLSH is 0.8-5 times of the optimal value of other methods, and it takes less time. Through the analysis of the distribution characteristics of experimental datasets, the experimental results show that LPLSH can take into account the efficiency, accuracy, and recall rate at the same time, can meet the robustness requirements of large-scale high- dimensional retrieval with multi-distribution characteristics in open environment.