Deep　learning　based　high　dynamic　range　(HDR　)　image　processing　algorithms　has　the　problem　of　skin　color　deviation　when　processing　images　containing　human　figures.　In　response　to　this　issue,　this　article　proposes　a　portrait　HDR　image　processing　algorithm　based　on　multi　feature　fusion-U(2)HDRnet.　This　algorithm　consists　of　three　parts:　skin　feature　extraction　module,　trilateral　feature　extraction　module　and　color　reconstruction　module.　Firstly,　the　skin　feature　extraction　module　separates　the　color　and　position　information　of　the　skin　region.　Secondly,　the　trilateral　feature　extraction　module　extracts　local　features,　global　features　and　semantic　features　of　the　image,　and　fuses　them　with　skin　features.　Finally,　the　color　reconstruction　module　interpolates　the　grid　in　terms　of　space　and　color　depth.　In　addition,　this　article　adds　an　improved　fusion　module　of　self　attention　and　convolution　to　improve　the　processing　performance　of　HDR.　At　the　same　time,　this　article　also　produces　the　PortraitHDR　dataset　for　portraits,　filling　the　gap　in　the　dataset　in　this　field.　The　test　results　show　that　the　PSNR　of　U(2)HDRnet　reaches　31.　42　dB,　and　the　SSIM　reaches　0.　985,　both　of　which　are　superior　to　the　commonly　used　HDR　algorithms.　They　obtain　high-quality　portrait　HDR　images　while　avoiding　skin　distortion.