Unmanned reclaimers in bulk materials ports has the problems of low reciprocating reclaiming efficiency. Meanwhile, the existing machine learning classification models are not effective because of high noise, frequent fluctuations, and unbalanced data of millimeter-wave radar sensing datasets. In this paper, a stack boundary sensing method based on improved fuzzy twin support vector machine combined with 1-Nearest Neighbor algorithm is proposed. Firstly, the millimeter-wave radar is used to obtain the stack boundary scan data and preprocess it. According to the spatial distribution and operation characteristics, the 10-dimensional features of the point cloud are extracted to form the stack point cloud sample dataset; secondly, the improved fuzzy membership function is introduced. The fuzzy twin support vector machine divides the pile point cloud sample dataset into overlapping and non-overlapping regions. Then, the fuzzy twin support vector machine decision boundary and 1-nearest neighbor algorithm are used to classify the non-overlapping and overlapping region samples respectively to improve the classification ability of unbalanced datasets. Finally, the classification results obtained are added to the perception link to achieve the purpose of sensing the boundary of the pile. Experiments on the dataset collected by manual operation radar show that the proposed perception method effectively improves the ability to recognize minority categories. Field experiments show that the improved perception method is closer to the operator′s judgment, the idle time of the bucket wheel is reduced by 15. 1% , which improves the operating efficiency of the unmanned reclaimer and has reference significance for the construction of unmanned bulk materials ports.