As　we　know,　image　colorization　is　widely　used　in　computer　graphics　and　has　become　a　research　hotspot　in　the　field　of　image　processing.　Current　image　colorization　technology　has　the　phenomenon　of　single　coloring　effect　and　unreal　color,　which　is　too　complicated　to　be　implemented　and　struggled　to　gain　popularity.　In　this　paper,　a　new　method　based　on　a　convolution　neural　network　is　proposed　to　study　the　reasonable　coloring　of　human　images　and　ensures　the　realism　of　the　coloring　effect　and　the　diversity　of　coloring　at　the　same　time.　First,　this　paper　selects　about　5000　pictures　of　people　and　plants　from　the　Imagenet　dataset　and　makes　a　small　dataset　containing　only　people　and　backgrounds.　Secondly,　in　order　to　obtain　the　image　segmentation　results,　this　paper　improves　the　U-net　network　and　carries　out　three　times　of　down　sampling　and　three　times　of　up-sampling.　Finally,　we　add　the　expanded　convolution,　and　use　the　sigmoid　activation　function　to　replace　the　ReLU　(The　Rectified　Linear　Unit)　activation　function　and　put　the　BN　(Batch　Normalization)　before　the　activation　function.　Experimental　results　show　that　our　proposed　image　colorization　algorithm　based　on　the　deep　learning　scheme　can　reduce　the　training　time　of　the　network　and　achieve　higher　quality　segmentation　results.