This topic is centered around temperature and stress, and describes the theory of electric power thermal characteristics. There are usually two methods for thermal coupling analysis, for direct coupling and sequential coupling. Considering that the stress field of the cable does not have much influence on the temperature field, it is proposed to use the sequential coupling method for the calculation of the thermal characteristics of the cable. The calculated and solved cable temperature and stress distribution values are put into the Lap-ML-ELM algorithm for training. When the contact coefficient k=1, 4, 7, 10, 13 and 15, the cable joints and surfaces produce a monotonically increasing law of temperature, and the stress exhibits the same situation.During the training of the model on the thermal characteristics of the cables, it is found that the accuracy curve of the thermal characteristics detection of the Lap-ML-ELM algorithm is higher than that of both the RNN network and the CNN network, which shows that in the detection of the thermal characteristics of cables, the Laplace Multilayer Extreme Learning Machine fusion algorithm performs more obviously.