In the early screening of colorectal cancer， diagnostic efficiency and accuracy can be improved by automated polyp detection and segmentation of colonoscopy images. Due to the complexity of internal environment of intestines and the limitation of image quality， automated polyp segmentation is still a challenging problem. Aiming at this problem， this paper proposes a dual-decoding model for polyp segmentation using Transformer and null convolution to achieve feature fusion （FTDC-Net）. ResNet50 is used as an encoder in order to be able to better extract deep image features. The Transformer coding module is used， which has a self-attention mechanism to capture long distance dependencies between the inputs， and different dilated-convolutions are used in the model to expand the sensory field of the model to allow the model to capture a larger range of information in the colonoscopy image. The decoding part of the network model in this paper uses a dual-decoding structure， including an autoencoder branch that reconstructs the inputs and a coding branch for segmenting the results. The output of the autoencoder is used in the model to generate an attention map as an attention mechanism. This map will be used to guide the segmentation results. Experimental validation is carried out on the Kvasir-SEG and ETIS-LARIBPOLYPDB standard datasets， and experimental results show that FTDC-Net can effectively segment colon polyps， and achieves a high level of improvement in all evaluation metrics compared to the current mainstream polyp segmentation models.