 The development of cities has led to urban congestion being nearly unavoidable for most large-scale cities. Road planning is an effective way to alleviate urban congestion, which is a classic NP-hard problem and has become an important research hot spot in recent years. A K-means clustering algorithm is an iterative clustering analysis algorithm that has been regarded as an effective means to solve urban road planning problems by scholars for the past few decades. However, it is difficult to determine the number of clusters and sensitively initialize the center cluster. To address this issue, a novel K-means clustering algorithm based on a noise algorithm was developed to capture urban hot spots in this paper. The noise algorithm was employed to randomly enhance the attribution of data points and output results of clustering by adding noise judgment in order to automatically obtain the number of clusters for the given data and initialize the center cluster. For unsupervised evaluation indexes, namely, DB, PBM, SC, and SSC, were directly used to evaluate and analyze the clustering results, and a non-parametric Wilcoxon statistical analysis method was employed to verify the distribution states and differences between clustering results. This article was authored by Xiaowan Ran, Xiangbing Zhou, Mulay, and others. We are article.tv, links in the description below.