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The all electric braking system of an aircraft plays a crucial role in the landing and takeoff stages. As the automation level of the system gradually increases, how to ensure the safety, stability, and reliability of its operation has become an urgent problem to be solved. The existing fault diagnosis methods have the problems of high average diagnostic error and long time consumption. A fault automatic diagnosis method for aircraft electric braking system is designed based on the improved Artificial Bee Colony Algorithm (ABC) optimized radial basis function network (RBF). The design adopts a hardware architecture of "USB interface+ARM+FPGA", consisting of a fault signal collector composed of an upper computer, signal attenuation circuit, etc., to implement fault signal acquisition for the aircraft"s all electric braking system. For collecting signals, design a signal denoising algorithm based on mutual information and Variational Mode Decomposition (VMD) to implement denoising processing. Using the improved ABC algorithm to optimize the parameters of the RBF neural network, ensuring the effectiveness of the optimization parameters. And introduce the concept of fuzzy sets to improve the performance of the network, use gradient descent method for network training updates, and reduce diagnostic result errors. Finally, input the denoised signal and use the optimized trained RBF neural network to achieve automatic fault diagnosis of the aircraft"s all electric braking system. The experimental test results show that the minimum deviation factor value of this method is 0.08 × 10-3. The average diagnostic iteration time for the three types of faults is relatively short, among which the average diagnostic iteration time for the "walking" fault of the main landing gear is the shortest.