The traditional dissolved gas analysis （DGA） in oil-immersed transformer fault diagnosis is unable to effectively utilize fault information, and the imbalance of fault sample types results in poor diagnostic model performance. To address this, a transformer fault diagnosis technique based on data augmentation and fault feature optimization, named SCNGO-SVM-AdaBoost, is proposed. Firstly, to handle the imbalanced sample dataset, the safe-level synthetic minority over-sampling technique （safe-level SMOTE） is used for data augmentation of the original transformer fault sample set. Then, kernel principal component analysis （K-PCA） is employed to optimize and extract fault features from the ratio-based oil chromatographic data. Secondly, the northern Goshawk optimization （NGO） algorithm is enhanced by integrating positive and negative cosine and refraction reverse learning strategies. The stability of this algorithm is verified using test functions and optimization capability is improved by the diagnostic algorithm. Through a comparative analysis of actual fault diagnosis using the original samples and other methods, the results show that this approach can effectively improve the performance of transformer fault diagnosis.