The insulation performance of transformer bushings is a crucial aspect that directly affects the safe operation of equipment. To diagnose the insulation status of transformer bushings and mitigate the impact of small-sample imbalanced data on diagnostic results, a particle swarm optimization combined with back propagation neural network (PSO-BPNN) and adaptive synthetic sampling (ADASYN) method are employed to fault diagnosis of transformer bushing. Initially, historical fault data of transformer bushings are gathered, and a sample set of dissolved gases in transformer oil with distinct fault categories is established. The ADASYN algorithm is used to synthesize the minority class samples in the original data, which allowed for obtaining balanced fault data. The balanced dissolved gases in oil served as the model input, and the fault status is used as the label output to diagnose the transformer bushings using the PSO-BPNN model. To diagnose the bushings under the original sample set, the back propagation neural network (BPNN), genetic combined with back propagation neural network (G-BPNN), cuckoo search combined with back propagation neural network (CS-BPNN), and PSO-BPNN models are used. The results reveal that the PSO-BPNN model based on ADASYN balanced data exhibited the highest accuracy among the various models for fault diagnosing the insulation status of transformer bushings. This approach effectively mitigate the impact of small sample imbalanced data on diagnostic results, and provide an effective method for assessing the insulation performance of transformer bushings.