With the advancement of transmission line inspection technology, infrared imaging has become increasingly crucial for detecting line faults and potential hazards. However, traditional image segmentation methods often struggle with accuracy when dealing with complex backgrounds and multispectral information. A two-dimensional maximum entropy threshold image segmentation algorithm based on multispectral fusion to enhance the segmentation performance of infrared images of transmission lines is proposed in the paper. The method involves fusing multispectral information obtained from infrared imaging sensors, and then applies the maximum entropy threshold algorithm to the fused image to accurately extract key features within the transmission lines. Experimental results show that the proposed algorithm achieves an image correlation coefficient of 0.907 8 and a structural similarity of 0.913 2. Compared with the traditional one-dimensional maximum entropy threshold algorithm, the correlation coefficient and structural similarity have improved by approximately 6.6% and 21.4%, respectively. Moreover, the relative error in image segmentation using the proposed method is reduced by more than 10% compared to the one-dimensional algorithm. This approach offers more reliable technical support for the intelligent inspection of transmission lines and holds significant practical application value.