K-means algorithm is a commonly used clustering algorithm and has been applied to traffic hotspot extraction. However, due to the number of clusters and the subjective setting of the initial clustering center, the traffic hotspots extracted by the existing clustering methods are often difficult to meet the requirements. Based on mutual information and divergence, an improved SK-means algorithm is proposed and applied to traffic hotspot extraction. In the proposed method, an initial clustering center is found based on mutual information between different points. In addition, the number of clusters is determined based on the ratio of mutual information and divergence. The proposed method is applied to the extraction of traffic hotspots in Chengdu for a certain period of time, and compared with the traditional K-means, the experimental results show that the proposed method has higher clustering accuracy and the extracted hotspots are more realistic.