Abstract: Ultra-dense networks meet the explosive traffic demand by densely deploying micro base stations, but at the same time, the interference is severe, so it is particularly important to allocate resources reasonably. In order to reduce interference and perform resource allocation, this paper proposes a resource allocation algorithm based on hybrid clustering. First, in order to solve the problem that the number of clusters and the center points of the clusters are difficult to determine in the traditional K-means algorithm, the Canopy algorithm is used for preprocessing. At the same time, when using the Canopy algorithm for preprocessing, the distance threshold is not directly set, and the weighted average formula is introduced to select the threshold, which can dynamically change the size and number of clusters according to the actual scene. Then, considering the user's service quality while maximizing the throughput, according to the optimization objectives and constraints, this paper proposes to use the Lagrangian dual algorithm to accurately calculate the sub-channels allocated by the micro base station to the user, and use the sub-gradient update algorithm to continuously update Lagrange multipliers to get the final assignment result of the sub-channels. Finally, in order to reduce energy consumption, instead of randomly assigning power to users, a water injection algorithm is used to assign power to users. The simulation results show that the proposed clustering algorithm distributes small cells in each cluster more accurately and evenly. On the premise of completing the clustering, the proposed resource allocation algorithm not only guarantees the user service quality, but also significantly improves the system throughput. quantity.