In　order　to　improve　consumers＇　efficiency　of　making　purchasing　decisions,　this　paper　proposed　a　product　attribute　extraction　method　based　on　seed-constrained-LDA　(Latent　Dirichlet　Allocation),　which　classify　and　extract　product　attributes　from　reviews,　so　that　reviews　can　be　displayed　according　to　different　product　attributes.　Specifically,　the　extraction　method　used　the　term　frequency-inverse　document　frequency　(TF-IDF)　algorithm　to　automatically　extract　keywords　as　an　attribute　seed　set.　Then,　it　reorganized　the　document　twice.　The　twice-reorganized　document　only　describes　one　product　attribute,　so　the　multi-attribute　co-occurrence　problem　of　long　text　and　the　sparsity　problem　of　short　text　can　be　solved,　and　the　reorganization　rate　of　document　can　be　improved.　Next,　the　must-link　and　cannot-link　seed　constraints　were　applied　to　define　the　probability　expansion　and　contraction　value,　which　affects　the　topic　allocation　of　the　LDA　model　and　makes　the　training　results　more　reasonable　by　constraints　on　the　Gibbs　sampling　process.　Finally,　the　topics　generated　by　the　seed-constraint-LDA　were　mapped　to　the　prior　attribute　categories.　The　results　of　qualitative　analysis　(attribute　categories,　attribute　words)　and　quantitative　analysis　(accuracy　rate,　entropy　value,　purity)　show　that　the　accuracy　and　purity　of　the　proposed　method　are　higher　than　the　existing　comparison　methods,　and　the　entropy　value　is　lower　than　that　of　the　existing　comparison　methods,　indicating　that　this　method　has　better　clustering　effect.　©　2022,　Editorial　Department,　Journal　of　South　China　University　of　Technology.　All　right　reserved.