Traditional multi-dimensional text clustering generally extracts features from text contents, but seldom considers the interaction information between users and text data, such as likes, forwards, reviews, concerns, references, etc. Moreover, the traditional multi-dimension text clustering mainly integrates linearly multiple spatial dimensions and fails to consider the relationship between attributes in each dimension. In order to effectively use text-related user behavior information, a Multi-dimensional Text Clustering with User Behavior Characteristics (MTCUBC) was proposed. According to the principle that the similarity between texts should be consistent in different spaces, the similarity was adjusted by using the user behavior information as the constraints of the text content clustering, and then the distance between the texts was improved by the metric learning method, so that the clustering effect was improved. Extensive experiments conduct and verify that the proposed MTCUBC model is effective, and the results present obvious advantages in high-dimensional sparse data compared to linearly combined multi-dimensional clustering.