Abstract: Clustering analysis is an important technology in data mining. By clustering analysis of multi-dimensional user behavior, it can help managers get more accurate and effective user evaluation information from the user level. In this paper, multi-dimensional user behavior features are extracted from user behavior data, and then unsupervised feature selection based on mutual information (UFS-MI) is used to sort, filter and confirm the features of the extracted features, and the weighted feature vectors of each user's behavior are obtained. The network is constructed according to the similarity between user behaviors, and then the user behavior network is clustered and analyzed by Blondel community partition algorithm. The experimental results on an empirical data set of a bus line show that the accuracy of the method is 92%, which is significantly higher than the accuracy rate of the traditional clustering algorithm K-means. The results can provide a reference for the management and training of the public transport management. This paper expands the application scope of network science in multi-dimensional user behavior data clustering analysis, enriches the idea of multi-dimensional driving behavior data clustering analysis, and provides reference for managers.