In view of the transparent objects inheriting information from the background and the limitation of receptive fields in traditional convolutional neural networks, a transparent object segmentation network TF-ME has been proposed based on Transformer and multi-scale feature enhancement. The model adopts a hybrid structure of CNN and Transformer. In the feature extraction stage, a multi-scale feature fusion module is designed to effectively integrate global and local information, thus improving the segmentation effect of the model on transparent objects of different sizes. In addition, the feedforward neural network is redesigned for an enhancement of the context understanding ability of the Transformer encoder, followed by comparative experiments conducted on the Trans10K-v2 dataset for a verification of the effectiveness of the proposed algorithm. The experimental results show that the proposed method achieves 94.68% ACC and 73.39% MIoU in 11 types of transparent object segmentation, respectively. Compared with other algorithms, the performance of the proposed model has been significantly improved.