Targeting at 3D point cloud data without any foreknowledge of information, this paper presents a new algorithm of point cloud simplification. Because of usual way of shooting in daily life, there often exist more detailed information in x-y direction in the point cloud.By using this feature, the proposed algorithm firstly selects x-y axis as the direction for division and computation and obtains x-y boundary. After observation of normal vector of point cloud, it is easy to find that if the normal vector of the points in the local region changes gently, it indicates that the region is relatively flat. On the contrary, if the normal vector changes greatly, it indicates that the region fluctuates greatly. Therefore, compute the arithmetic mean of the included angle between the normal vector of one point in the point cloud and the normal vector of its k-neighborhood point. Define the feature of that point, and based on this, extract key feature points in data. Finally, the gridding method is used to divide the scattered point cloud data whose boundary and key points have been extracted and thus finish simplification. Experimental results show the effectiveness of the proposed algorithm.