The research on uncertain data clustering algorithms generally assumes that uncertain data obeys a certain distribution, so we can obtain the probability density function or probability distribution function which represents the uncertain data. However, it is difficult to guarantee the consistency between the assumed distribution and the
distribution of uncertain data in practical applications. Existing algorithms based on density are sensitive to initial parameters, so they cannot find class clusters of arbitrary density when clustering uncertain data with uneven density. In view of these shortcomings, we propose an algorithm based on interval number for uncertain data object sorting recognition clustering structure (UDOPTICS). It uses the interval number theory and the statistical information of the uncertain data to represent the uncertain data more reasonably. We propose the concept and calculation method of interval core distance and interval reachable distance with low computational complexity, which are used to measure the similarity between uncertain data and expand the cluster structure of clusters and object sorting. This algorithm can well find clusters of arbitrary density. Experimental results show that the UDOPTICS algorithm has higher clustering accuracy and lower complexity.