## 1 Introduction
Given that points located in the same discontinuity in a point cloud often share similar characteristics, mainly including 3D coordinates, normal vectors, curvature, and color, another approach involves employing machine learning (ML) techniques for classification based on extracted features. For point cloud feature extraction, 3D coordinates and colors are directly available through laser scanning and photogrammetry [30]. For normal vectors and curvature, some studies have utilized methods such as triangulation [15; 18; 31], cube search [8], and other methods for extraction, but compared to the utilization of 3D methods to extract from a point set composed of points and neighboring points [32; 33], the former methods may lose a certain amount of point cloud information. Some scholars have utilized normal vectors as features and applied the K-means algorithm [18; 34] to cluster the point cloud, followed by the extraction of discontinuities from the clustered result. The Fuzzy C-means (FCM) algorithm, an improvement of K-means, calculates the probability that a sample point belongs to each cluster center, subsequently determining the category of sample points to achieve the automatic identification of discontinuities, and so exhibits reduced sensitivity to noise and superior performance in terms of classification results [35; 36]. Nevertheless, both of the aforementioned methods necessitate a pre-determination of the cluster count and exhibit sensitivity to the initial selection of cluster centers. Variations in the choice of initial cluster centers frequently lead to discrepancies in the classification results. At this point, some research has introduced parameters such as Minimum Description Length (MDL) [37], and clustering validity indices [18; 35], to provide references for determining the number of clusters. Optimization algorithms, including Particle Swarm Optimization (PSO) [38], Differential Evolution (DE) [39], and the Firefly Algorithm (FA) [40], have been introduced to optimize the update of cluster centers. [PERSON] et al. [41] proposed a new fast fuzzy clustering (FFC) method by combining the FCM algorithm with peak density clustering (CFSFDP), overcoming the respective defects of the two algorithms. [PERSON] et al. [32] integrated Density-Based Spatial Clustering of Applications with Noise (DBSCAN), an unsupervised machine learning clustering algorithm based on density that is adept at handling irregular shapes, with Kernel DensityEstimation (KDE) for the extraction of discontinuities from point clouds. [PERSON] et al. [PERSON] et al. (2019) proposed five features for describing the points after filtering the noisy points in the point cloud using various methods, combined with K-Medoids and DBSCAN to complete the extraction of the discontinuity, which demonstrated strong adaptability in complex scenes. [PERSON] et al. [PERSON] et al. (2019) proposed an adaptive DBSCAN (ADBSCAN) algorithm to address the challenge of determining DBSCAN parameters, which further improves the automation of discontinuity extraction. Distinct from unsupervised machine learning algorithms, [PERSON] et al. [PERSON] et al. (2019) employed a well-trained artificial neural network model, trained by the manual selection of an ample training dataset, to classify point clouds, greatly enhancing the efficiency of discontinuity extraction. However, achieving a well-trained network involves repeated manual sample selection until the accuracy is satisfied, thereby imposing significant constraints on the level of automation and computational efficiency. To address the aforementioned issues, this paper proposes a semi-automated approach that efficiently generates a training sample set in a simple manner. The Naive Bayes classifier (Kal