In response to the fact that the accuracy of traditional transformer intelligent diagnosis models based on dissolved gas analysis (DGA) data is easily affected by the selection of input feature dimensions and hyperparameters, this study proposes a transformer intelligent fault diagnosis model based on the combination of real domain rough sets and NRBO-XGBoost. Firstly, a feature extraction algorithm with adaptive performance is proposed based on the concept of real domain rough set for extracting initial fault features of transformers; secondly, in response to the limitation of XGBoost being affected by hyperparameter selection in transformer fault diagnosis, this study utilizes the high convergence speed and effective avoidance of local optima of the NRBO algorithm to globally optimize the hyperparameters of XGBoost, and proposes the NRBO-XGBoost model for transformer fault diagnosis; finally, through multiple experimental comparisons, compared with other traditional features, the performance of the feature extraction algorithm proposed in this study has been improved in various classifiers, proving that the feature extraction algorithm proposed in this paper can effectively extract information from the features to enhance the performance of the model. Moreover, NRBO-XGBoost achieves an accuracy of 92.09% in transformer fault diagnosis with only 20 convergences compared to other comparative models, demonstrating superior performance.