The application of artificial intelligence technology to the field of fault diagnosis can realize the automation and intelligent diagnosis of power equipment and improve diagnosis accuracy and efficiency. Taking the single-input multiple-output flyback switching power supply as an example, for the problem of abnormal circuit performance caused by the failure of its fragile components, a non-intrusive switching power supply fault diagnosis method fusing the input current and output voltage information is proposed by analyzing the signal characteristics and divisibility of different fault modes. A multidimensional feature vector fusing time-frequency domain information consisting of time-domain features and frequency-band wavelet packet singular entropy features is constructed, and the mapping relationship between fault features and fault modes is established. Then, a deep neural network (DNN) fault diagnosis method based on artificial intelligence technology is proposed to monitor the operation status of the flyback switching power supply in real time, identify the fault location in time through data analysis, and provide early warning for potential faults. The experimental results show that the method proposed in this paper has a good diagnostic effect on both single-fault and multi-fault modes, the diagnostic accuracy can reach 97.9%, and the method can show high diagnostic accuracy and strong anti-interference performance under different working conditions.