In　this　paper,　a　network　structure　is　proposed　for　the　task　of　single　person　pose　estimation　in　a　complex　environment.　This　method　improves　the　stacked　hourglass　model,　achieves　the　feature　extraction　on　most　scales,　and　raises　the　detection　accuracy　of　human　key　points.　In　the　hourglass　module,　we　use　convolution　operation　to　complete　the　upsampling　to　get　more　semantic　information.　When　the　responses　of　the　two　residual　elements　are　added,　we　replace　the　identity　mapping　in　the　residual　element　with　the　1×1　convolution　element　module　to　improve　the　phenomenon　of　variance　explosion.　We　conducted　model　evaluation　experiments　on　MPII　and　LSP　data　sets,　and　the　results　showed　that　the　average　detection　accuracy　of　key　points　was　improved　by　0.2%　and　0.8%　respectively　through　our　improvement　on　the　stacked　hourglass　model.　©　2019　Association　for　Computing　Machinery.