In order to balance the robustness and building complexity of a feature descriptor, a local feature description algorithm based on Laplacian is presented. It is analyzed and illustrated that the Laplacian not only has good properties to Euclidian transformation, zoom, and linear brightness changes of an image, but also can characterize the local structure of the image. On the basis of that, a 64-dimension descriptor is built with the response of Laplacian of Gaussian. Finally, the descriptor is used to match feature points with the absolute distance as similarity measurement. Simulation results indicate that the proposed descriptor can obtain better matching results for the image with zoom, rotation, blurring, illumination varying as well as smaller viewpoint changes, and the matching speed is more than 4 times that of Scale Invariable Feature Transformation(SIFT). The proposed feature description algorithm is suitable for matching the images of structured scenes, for it is insensitive to the image transformation with rotation, zoom, luminance varying, compression or small viewpoint changes.