With the acceleration of social development and urbanization, birds’ natural habitats have been greatly disturbed and threatened. Satellite tracking technology can collect much bird activity data, providing important data support for habitat protection research. However, satellite data are usually characterized by discontinuity, extensive periods, and inconsistent frequency, which challenges cluster analysis. Habitat research frequently employs clustering techniques, but conventional clustering algorithms struggle to adjust to these data features, particularly when it comes to time dimension changes and irregular data sampling. T-DBSCAN, an enhanced clustering algorithm, is suggested to accommodate this intricate data need. T-DBSCAN is improved based on the traditional DBSCAN algorithm, which combines a quadtree structure to optimize the efficiency of spatial partitioning and introduces a convex hull algorithmic strategy to perform the boundary identification and clustering processing, thus improving the efficiency and accuracy of the algorithm. T-DBSCAN is made to account efficiently for the uniformity of data sampling and changes in the time dimension. Tests demonstrate that the algorithm outperforms conventional habitat identification accuracy and processing efficiency techniques. It can also manage large amounts of discontinuous satellite tracking data, making it a dependable tool for studying bird habitats.