Transformer oil is an important liquid insulating medium in oil-immersed transformer. The interfacial tension value of oil is closely related to the degree of oil deterioration. Based on the multi-frequency ultrasonic detection technology

a predictive model designed to detect variable pressure oil interfacial tension by using the back propagation neural network（BPNN） which is optimized by the genetic algorithm（GA） was established. Data samples were collected from 160 sets of transformer oil samples

150 of which were training sets and 10 were forecast sets. Based on the theory of molecular properties of different interfacial tension and multi-frequency ultrasonic testing

the 242 dimensions multi-frequency ultrasonic testing data were adopted as the input

and interfacial tension of transformer oil as the output. The BP neural network hidden layer neurons number was determined to be 14 in the experiment

thus a nonlinear mapping relationship was established

and the genetic algorithm（GA） was adopted to optimize the BP neural network connection weights and threshold of every layer. The results show that the mean absolute percent error of oil interfacial tension prediction model based on GA-BPNN is 9.76%. Compared with the results from traditional BPNN oil interfacial tension prediction models

the prediction results are higher and the error is smaller. The research results can lay the foundation for monitoring the interfacial parameters such as the interfacial tension of transformer oil based on multi-frequency ultrasonic detection technology.