The distribution network is directly oriented to terminal users, which can guarantee the reliability of residential and industrial production power supply. The distribution transformer is an important equipment of the distribution network. It has a wide distribution range and a large quantity, so the safe and reliable operation of the distribution network is the key to guaranteeing users' normal production and living. However, judging from the accidents that have occurred in recent years, many transformer accidents do not have any symptoms before they occur. This shows that the current routine test projects and test cycles still have certain limitations, and some accident precursor information cannot be captured in time. Taking into account the limitations of traditional detection methods, it is necessary to add effective monitoring methods to distribution transformers in a timely manner. This paper proposes a transformer oil condition monitoring method based on multi-frequency ultrasound. We obtained oil samples from 88 transformers that are running in the substation. First, in the laboratory, the oil samples are tested for the breakdown voltage and dielectric loss factor, micro water and acid value these four parameters. According to the multi-indicator comprehensive analysis, the status of the selected transformer oil samples is classified, and then a new type of multi-frequency ultrasonic equipment is used. Separately fired a beam of ultrasonic waves of different frequencies into each set of oil samples, and various parameters of ultrasonic waves obtained by the ultrasonic receiver module in real time. Then utilize the neural network to process ultrasonic parameters (wave speed, amplitude and phase angle of 240-dimensional data of the three-phase ultrasonic waves at 40 frequency points) and establish the relationship between ultrasonic spectrum parameters and the transformer oil state. The mapping relationship enables real-time, accurate and comprehensive monitoring of the state of transformer insulation oil.