To address the user cluster partitioning issue in the deployment strategy of Unmanned Aerial Vehicle （UAV） base stations for auxiliary communication in emergency scenarios， a feature-weighted fuzzy clustering algorithm， named Improved FCM， was proposed by considering both the performance of UAV base stations and user experience. Firstly， to tackle the problem of high computational complexity and convergence difficulty in the partitioning process of user clusters under random distribution conditions， a feature-weighted node data projection algorithm based on distance weighting was introduced according to the performance constraints of signal coverage range and maximum number of served users for each UAV base station. Secondly， to address the effectiveness of user partitioning when the same user falls within the effective ranges of multiple clusters， as well as the maximization of UAV base station resource utilization， a value-weighted algorithm based on user location and UAV base station load balancing was proposed. Experimental results demonstrate that the proposed methods meet the service performance constraints of UAV base stations. Additionally， the deployment scheme based on the proposed methods effectively improves the average load rate and coverage ratio of the system， reaching 0.774 and 0.026 3 respectively， which are higher than those of GFA （Geometric Fractal Analysis）， Sp-C （Spectral Clustering）， etc.