Transformers are very important for the transmission and distribution of electricity,but due to changes in load and the influence of the working environment,various faults may occur in transformers.To accurately and quickly detect faults in transformers,conduct effective fault diagnosis and equipment maintenance,this study solves the problems of data imbalance and temporal data in transformers by introducing a long short-term memory network with fatigue factors.In addition,a fusion model is ultimately constructed by combining the recursive all-pair field transformation streamer method to achieve more accurate and robust optical flow estimation in the model.The experiment indicated that the maximum accuracy of the predicted values combined with the model was around 95%,and the minimum was around 35%.Compared to other models,the maximum accuracy of actual values was around 80%, and the minimum accuracy was better at 10%.In the application experiment,the frequency of insulation faults was the least obvious, with only 10 faults.The resistance fault was evident,with a total of 100 faults.The combined model could well reflect the fluctuation of fault current and the collection of fault number by different sensors.Therefore, the proposed model has high accuracy,good precision, and outstanding application effects,which can provide new ideas for the construction of intelligent transformer anomaly detection models.