The invention is applicable to the technical field of transformers, and provides a method and terminal device for predicting the winding hot-spot temperature of a transformer. The method comprises thesteps of collecting temperature data of a winding hot-spot of the transformer, and enabling the collected temperature data to serve as sample data; training an artificial neural network model according to a particle swarm algorithm and the sample data to obtain optimal model parameters of the artificial neural network model, and enabling the artificial neural model based on the optimal model parameters to serve as a prediction model; collecting operating parameters and structural parameters of the transformer; and performing winding hot-spot temperature prediction on the transformer accordingto the operating parameters, the structural parameters and the prediction model. According to the invention, the particle swarm algorithm is introduced into parameter optimization for the artificialneural network model, and prediction is performed on the winding hot-spot temperature of the transformer by using the optimized prediction model, so that the accuracy of prediction obtained by the prediction model for the winding hot-spot temperature of the transformer can be improved.