In transformer fault diagnosis accuracy, addressing the limitations of traditional neural networks such as insufficient interpretability and weak temporal feature extraction capabilities, this study proposes a novel diagnostic model,LKAN which integrates Long Short?Term Memory (LSTM) with Kolmogorov?Arnold Network (KAN). The model first employs LSTM to model time?series data from transformer operations, extracting hidden states as temporal features. These features are then fed into the KAN layer, where B?spline functions enable nonlinear mapping and function decomposition, thereby enhancing both the model's expressiveness and interpretability. Experimental results on real?world power transformer datasets demonstrate that the LKAN model achieves a diagnostic accuracy of 98.80%, outperforming LSTM, Convolutional Neural Network（CNN）, Gated Recurrent Unit（GRU）, and single KAN models.Meanwhile, it exhibits strong generalization ability and stability. The LKAN model effectively integrates the temporal modeling capability of LSTM and the interpretability advantage of KAN. It provides a technical path with high accuracy and strong interpretability for intelligent fault diagnosis of transformers, and has good engineering promotion value.