Accurate　segmentation　of　lesions　in　lung　CT　images　remains　challenging　due　to　blurred　boundaries,　small　lesion　sizes,　and　the　scarcity　of　annotated　data.　To　address　these　issues,　this　paper　proposes　a　semi-supervised　contrastive　learning　framework　with　a　novel　multiple　attention　UNet　(MA-UNet)　for　lung　CT　image　segmentation.　The　MA-UNet　integrates　a　dual-attention　module　(DAM)　and　attention　gates　(AGs)　to　enhance　spatial-channel　feature　refinement　and　boundary　sensitivity.　The　DAM　captures　global　context　and　channel-wise　dependencies,　while　the　AG　emphasizes　lesion-related　features.　Furthermore,　residual　blocks　are　used　to　improve　gradient　propagation　and　computational　efficiency.　To　overcome　limited　annotations,　we　propose　a　contrastive　learning　framework　that　can　fully　utilize　both　labeled　and　unlabeled　data　to　improve　segmentation　accuracy.　To　verify　the　validity　of　the　methods　and　parameters　design　in　this　paper,　we　systematically　carry　out　multiple　ablation　experiments.　The　experimental　results　show　that　the　Dice,　MIoU　and　Recall　scores　of　MA-UNet　based　on　comparative　learning　with　only　1/2　ratio　of　labeled　data　are　78.41%,　88.78%　and　91.79%,　respectively,　which　are　close　to　its　supervised　segmentation　model,　which　effectively　overcomes　the　problem　of　lack　of　labeled　data.