Aiming at the prediction accuracy of transformer hot spot temperature, the ant colony algorithm ( ACO) combined with improved principal component analysis ( IPCA) was proposed to optimize BP neural network model to predict hot spot temperature. Firstly, IPCA is used to remove data redundancy information and solve the correlation between parameters to improve the ability of network generalization. In order to avoid BP neural network that is easily falling into local optimum and slow convergence speed, ACO was used to optimize the weights and thresholds of the network to speed up the algorithm and improve the prediction accuracy. Verified by the measured transformer temperature data, the mae, mse and mape indexes in the predicted results are 0. 065 7, 0. 006 7 and 0. 44%, respectively. The prediction accuracy and network performance are better than those of IEEE, BP and IPCA-BP models, thus verifying the validity and feasibility of the proposed model.