In the process of laser point cloud boundary normal vector extraction , the point cloud boundary data is difficult to accurately register , resulting in poor extraction accuracy and efficiency. In order to analyze the laser scan- ning information more accurately , a method of extracting the normal vector of laser point cloud boundary based on big data mining is proposed. This method first calibrates the laser point cloud through the camera to obtain the laser point cloud image , and then de-noises the acquired image through the principal component analysis , surface fitting and fil- tering algorithm. Finally , the image is processed by graying and Gaussian filtering. At the same time , the gray center in the normal direction of the image center point is extracted by combining the Otsu threshold method , so as to achieve the extraction of the normal vector of the laser point cloud boundary. The experimental results show that the included angle feature of normal vector extracted by the proposed method is basically consistent with the included angle feature in the ideal state , and the extraction efficiency is high and the iteration error is small.