Aiming at the problems of insufficient data reliability and resource waste in the decision of redundant data of UAVs, a compensation algorithm for UAV IMU multi-sensor redundancy based on BP neural networks is proposed. The low-precision IMU sensor data is input to the BP neural network, and the non-linear fitting capability of the BP neural network is used to compensate for errors in low-precision IMU data, then use data arbitration algorithm based on confidence to arbitrate multiple higher-precision data and output the sensor data after data fusion. This process can also judge and locate sensor faults. The singularity problem can be solved by changing the installation method of similar sensors. The experimental results prove that after neural network error compensation, the error is reduced by 55. 2%. Furthermore, the error after neural network error competition is 53. 9% smaller than the error after using the kalman filter algorithm for error compensation. The algorithm takes full advantage of redundant sensor design, improves the reliability of the sensor system.