Core　user　demands　of　wardrobe　furniture　design　are　becoming　increasingly　complex.　Traditional　design　methods　fail　to　systematically　analyze　the　interrelationships　among　these　multidimensional　factors.　This　study　integrated　web　text　mining,　the　Decision-Making　Trial　and　Evaluation　Laboratory　(DEMATEL)　method,　and　the　Analytic　Network　Process　(ANP)　to　construct　a　causal　network　model　for　wardrobe　design,　and　further　optimized　design　proposals　through　the　Technique　for　Order　Preference　by　Similarity　to　Ideal　Solution　(TOPSIS).　By　applying　Python　technology,　user　evaluation　data　were　extracted　from　mainstream　e-commerce　platforms,　with　high-frequency　user　demand　keywords　being　identified　and　categorized　into　four　key　dimensions.　DEMATEL　was　employed　to　quantify　the　causal　intensity　and　centrality　of　the　identified　factors;　ANP　was　subsequently　utilized　to　construct　a　network　hierarchy,　revealing　the　feedback　mechanisms　between　functional　modules　and　user　experience.　Finally,　TOPSIS　was　applied　to　rank　three　design　proposals,　among　which　Option　3—featuring　flexible　space　partitioning,　auto-sensing　lighting,　and　anti-tip　design—　was　selected　as　the　optimal　solution.　The　findings　demonstrate　that　integrating　text　mining　with　the　DEMATEL-ANP-TOPSIS　framework　can　effectively　identify　the　prioritization　of　user　needs,　thereby　providing　scientific　decision　support　for　furniture　design.　©　2025,　North　Carolina　State　University.　All　rights　reserved.