The　generative　adversarial　network　(CAN)　provide　a　new　way　for　semantic　image　inpainting　problem.　The　missing　semantic　information　can　be　predicted　by　generating　an　image　with　similar　distribution　of　corrupted　image　based　on　GAN.　In　this　paper,　we　propose　a　high　vision　quality　semantic　inpainting　algorithm　based　on　a　LS-DCGAN.　We　discuss　the　optimization　of　GAN　training　and　introduce　the　least　squares　loss　function　to　solve　the　vanishing　gradient　problem　of　DCGAN.　Based　on　a　trained　LS-DCGAN,　we　propose　a　new　adversarial　loss　function　for　optimizing　inpainting　network　input.　Experiment　on　two　datasets　show　that　our　algorithm　is　stable　and　effective,　and　have　higher　naturalness,　validity　and　semantic　similarity　on　visual　experience　than　the　state-of-the-art　algorithms.