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Scene reconstruction can provide global information and local details for the autonomous operation of intelligent agricultural machinery. Aiming at the problem of poor feature description and insufficient point cloud registration accuracy caused by the lack of high-level structure of points, lines and planes on the surface of field, a solution based on point cloud registration method for farmland surface based on contour features of rotating surface was proposed. Firstly, the 32-line LiDAR was used to obtain real surface point cloud data of the field and complete pre-processing such as denoising and down-sampling;then, singular value decomposition of weighted linear covariance calculation matrix was used to determine the unique local reference coordinate system of key points, and the distance information of the intersection between the key points and the rotating surface section was calculated to generate the local feature descriptor of the surface point cloud;finally, a multi-level feature matching strategy based on the principle of single feature primary selection and local feature fine matching was used to perform local feature matching, and the rotation matrix and translation matrix were calculated to complete the point cloud registration. The analysis results showed that compared with other methods, the average accuracy of the contour feature of the rotating surface was increased by 7.5 percentage points, and the average recall rate was increased by 24.09 percentage points;compared with the nearest neighbor search, the multi-level feature matching strategy increased the average accuracy by 12.68 percentage points and the average recall rate by 18.38 percentage points;the point cloud registration method proposed had an average translation error of 23.59dr, an average translation error of 3.72°, and a registration success rate of 87.5%. Therefore, the proposed field surface point cloud registration method based on the contour feature of the rotating surface was suitable for the automatic registration of the real agricultural surface disorder point cloud.