Infrared image segmentation plays a pivotal role in diagnosing faults in electrical equipment using infrared imagery. However, uneven heat dissipation, lower contrast, and interference from multiple sources of noise in electrical equipment can lead to over-segmentation of the target region, seriously affecting segmentation accuracy. In this study, we propose an Enhanced Fuzzy C-Means (EnFCM) clustering method based on fusion reconstruction for infrared image segmentation of electrical equipment. First, the gradientimage was subjected to an adaptive morphological reconstruction operation to ensure the segmentation ability of the algorithm on noisy images; second, the image was tested for saliency, and the reconstructed image was obtained by fusing the saliency map with the gradient map to highlight the features of the fault site and avoid over-segmentation; then, watershed segmentation was performed on the reconstructed image to obtain the super-pixel image; finally, the histogram clustering of the super-pixel image was obtained by segmentation. The experimental results on the infrared image of electrical equipment show that the algorithm in this paper can accurately segment the fault area on it, obtain its location and contour, and effectively improve the phenomenon of over-segmentation and in the comparison of the selected intersection and concatenation ratio and DICE coefficient indexes, this paper's method improves 81% and 79% on average compared to selected FRFCM, FCM, SFFCM, FCM_SICM, RSSFCA, and AFCF; meanwhile, it is extremely robust to noise, and in the comparison of selected segmentation accuracy indexes, this paper's method achieves segmentation results that are on average 73% superior compared to selected FRFCM, FCM, SFFCM, FCM_SICM, RSSFCA, and AFCF, thus, superior segmentation results were achieved.