Most clustering algorithms require setting one or more parameters, which rely on prior knowledge or are constantly adjusted based on external indicators. To address the issues of requiring external index guidance, blindness, and time-consuming parameter setting for clustering algorithms on complex data, we propose a novel Parameter-Adaptive Border Peeling clustering algorithm (PABP). The PABP algorithm initially employs the maximum number of neighbors identified through natural neighbor search to automatically ascertain the number of local neighborhoods. At the same time, the Gaussian kernel bandwidth can be adaptively obtained in density measurement, which can highlight high-density areas. Secondly, the number of peels is adaptively determined by the coefficient of variation of density during the iterative border peeling process. Lastly, labels are assigned to core points based on graph connections, while the clustering of border points is accomplished via label propagation. PABP does not require users to adjust parameters based on prior knowledge or external indicators throughout the entire process. In the experiment, PABP was compared with seven other advanced clustering algorithms on 13 synthetic datasets, 10 UCI datasets, and Olivetti Face and MNIST datasets. The results indicate that the clustering performance of PABP is superior to the compared algorithms.