Traditional　human　pose　estimation　methods　typically　rely　on　complex　models　and　algorithms.　Lite-HRNet　can　achieve　an　excellent　performance　while　reducing　model　complexity.　However,　its　feature　extraction　scale　is　relatively　single,　which　can　lead　to　lower　keypoints’　localization　accuracy　in　crowded　and　complex　scenes.　To　address　this　issue,　we　propose　a　lightweight　human　pose　estimation　model　based　on　a　joint　channel　coordinate　attention　mechanism.　This　model　provides　a　powerful　information　interaction　channel,　enabling　features　of　different　resolutions　to　interact　more　effectively.　This　interaction　can　solve　the　problem　of　human　pose　estimation　in　complex　scenes　and　improve　the　robustness　and　accuracy　of　the　pose　estimation　model.　The　introduction　of　the　joint　channel　coordinate　attention　mechanism　enables　the　model　to　more　effectively　retain　key　information,　thereby　enhancing　keypoints’　localization　accuracy.　We　also　redesign　the　lightweight　basic　module　using　the　shuffle　module　and　the　joint　channel　coordinate　attention　mechanism　to　replace　the　spatial　weight　calculation　module　in　the　original　Lite-HRNet　model.　By　introducing　this　new　module,　we　not　only　improve　the　network　calculation　speed　and　reduce　the　number　of　parameters　of　the　entire　model,　but　also　ensure　the　accuracy　of　the　model,　thereby　achieving　a　balance　between　performance　and　efficiency.　We　compare　this　model　with　current　mainstream　methods　on　the　COCO　and　MPII　dataset.　The　experimental　results　show　that　this　model　can　effectively　reduce　the　number　of　parameters　and　computational　complexity　while　ensuring　high　model　accuracy.