Deep　learning　technology　can　improve　sensing　efficiency　and　has　the　ability　to　discover　potential　patterns　in　data;　the　efficiency　of　user　behavior　recognition　in　the　field　of　smart　homes　has　been　further　improved,　making　the　recognition　process　more　intelligent　and　humanized.　This　paper　analyzes　the　optical　sensors　commonly　used　in　smart　homes　and　their　working　principles　through　case　studies　and　explores　the　technical　framework　of　user　behavior　recognition　based　on　optical　sensors.　At　the　same　time,　CiteSpace　(Basic　version　6.2.R6)　software　is　used　to　visualize　and　analyze　the　related　literature,　elaborate　the　main　research　hotspots　and　evolutionary　changes　of　optical　sensor-based　smart　home　user　behavior　recognition,　and　summarize　the　future　research　trends.　Finally,　fully　utilizing　the　advantages　of　cloud　computing　technology,　such　as　scalability　and　on-demand　services,　combining　typical　life　situations　and　the　requirements　of　smart　home　users,　a　smart　home　data　collection　and　processing　technology　framework　based　on　elderly　fall　monitoring　scenarios　is　designed.　Based　on　the　comprehensive　research　results,　the　application　and　positive　impact　of　optical　sensors　in　smart　home　user　behavior　recognition　were　analyzed,　and　inspiration　was　provided　for　future　smart　home　user　experience　research.　©　2024　by　the　authors.