Currently,there are two problems in segmenting lung cancer from computed tomography (CT) images.One is that the size and shape of the lesions are different,and the other is that the quantity of labeled data is small.To figure out those above issues,this paper proposes a dual attention semi-supervised learning network (SDA-Net) for lung cancer segmentation algorithm.Firstly,the residual-dense blocks (RDBs) are added to the encoding stage of U-Net for feature extraction to retain more shallow features as much as possible.Secondly,the semantic relevance of the same class features is integrated at the end of the encoding stage using a dual attention mechanism containing position attention and channel attention to enhance the feature representation of the target.Finally,to address the problem of small quantity of labeled data,a two-path consistent semi-supervised learning (SSL) method is used to enable the dual attention network to be trained with both labeled and unlabeled data,which significantly improves the segmentation performance of network.The test results show that the Dice similarity coefficient, Jaccard index,sensitivity and precision of the proposed method achieved 0.843 2,0.733 1,0.809 2 and 0.886 1,respectively,which outperforms the current typical segmentation algorithms.