Understanding　and　interpreting　vast　amounts　of　information　is　pivotal　in　the　contemporary　data-rich　age.　Data　visualization　has　emerged　as　a　significant　measure　of　comprehending　these　data.　Similarly,　an　appropriate　visualization　can　also　enhance　software　modeling　by　providing　straightforward　and　interactive　representations.　However,　current　data　visualization　methods　predominantly　require　users　to　have　data　visualization-related　expertise,　which　is　usually　challenging　to　obtain　in　reality.　It　is　essential　to　bridge　the　gap　between　visualization　requirements　and　visualization　solutions　for　non-expert　users,　assisting　them　in　automatically　operationalizing　their　visualization　requirements.　This　paper　proposes　a　MUltilayer　framework　for　analyzing　and　operationalizing　visualization　REQuirements　that　automatically　derives　appropriate　visualization　solutions　based　on　users＇　requirements.　Specifically,　we　systematically　investigate　the　connections　among　visualization　requirements,　visual　variable　characteristics,　visual　variable　attributes,　and　visualization　solutions,　based　on　which　we　establish　a　conceptual　framework　that　characterizes　the　relationships　among　different　layers.　Our　proposal　contributes　to　not　only　automatically　operationalizing　visualization　requirements　but　also　providing　meaningful　explanations　for　the　derived　visualization　solutions.　To　promote　our　proposal　and　pragmatically　benefit　real　users,　we　have　developed　and　deployed　a　prototype　tool　based　on　the　proposed　framework,　which　is　publicly　available　at　https://reqdv.vmasks.fun.　To　evaluate　our　proposed　framework,　we　conducted　an　initial　controlled　experiment　with　44　participants　to　test　the　performance　of　the　evolved　mappings　within　our　framework.　Based　on　the　expert＇s　feedback,　we　refined　the　mappings　and　incorporated　a　ranking　system　for　visualization　solutions　tailored　to　specific　requirements.　To　assess　the　current　method,　a　subsequent　experiment　with　another　group　of　44　participants　and　a　focused　case　study　involving　two　new　participants　were　carried　out.　The　results　demonstrate　that　users　perceive　that　the　current　method　accelerates　task　completion,　especially　for　complex　tasks,　by　efficiently　narrowing　down　options　and　prioritizing　them.　This　approach　is　particularly　advantageous　for　users　with　limited　data　visualization　experience.　Besides,　the　multilayer　framework　can　be　used　to　inspire　the　visualization　of　models　in　the　software　modeling　community.