Tail rotor bearing is the key components of Unmanned Aerial Vehicle (UAV) tail rotor to help the tail rotor blade to keep the balance and control the course. The previous researches about tail rotor bearing diagnosis are based on vibration, acoustic, temperature and others. However, the existing monitoring signals are easily contaminated by environment or measurement noises and fail to detect early fault with high accuracy. To overcome this challenge, this paper utilizes the ultrasound signal to monitor health condition of tail rotor bearing, and proposes an automatic feature extraction and fusion method. In our proposed method, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is first used decompose the ultrasound signal into several intrinsic mode functions (IMFs) containing different fault-related information. Then, we design an attention-based weight module to dynamically assign different weights to each IMFs. Finally, the weighted IMFs are fed to a CNN-based feature extraction module to perform feature learning and classification. A Unmanned Aerial vehicle (UAV) rotor simulation bench has been setup to validate the performance of our proposed method. Experimental results illustrate that the proposed approach can effectively decompose and extract the discriminative fault features of Unmanned Aerial vehicle (UAV) tail rotor bearing and achieved superior diagnosis performance.