Multi-Label　Text　Classification　(MLTC)　is　a　task　to　assign　documents　to　one　or　more　class　labels.　Most　of　the　latest　approaches　are　based　on　deep　learning.　However,　among　the　various　types　of　methods,　there　are　still　defects　in　capturing　fine-grained　information　in　the　text　and　in　modeling　the　dependencies　between　labels.　In　recent　years,　neural　network　model-based　approaches　have　made　great　progress,　among　which　the　BERT　language　model　can　capture　bidirectional　contextual　information　well　but　cannot　capture　label-specific　semantic　information,　so　we　propose　a　new　multi-label　text　classification　framework　LAGAT　by　combining　attention　mechanism　for　feature　learning.　This　framework　is　devoted　to　mining　inter-label　dependencies　by　constructing　graphs　of　label-specific　text　semantic　information　and　using　the　graph　attention　network　(GAT)　to　learn　the　relative　importance　between　labels　by　updating　the　weights　on　the　edges,　thus　mining　the　inter-label　dependencies.　In　further　experiments,　we　confirm　that　the　GAT　is　weaker　than　the　self-Attention　mechanism　in　mining　text　labels　by　aggregating　neighborhood　information　at　moderate　label　size,　and　propose　an　improved　model　framework　LA-Trans,　which　also　utilizes　label　association　relations　with　text　semantic　information,　focuses　on　the　target　region　and　ignores　irrelevant　information,　and　achieves　the　performance　of　state　of　the　arts.　©　COPYRIGHT　SPIE.　Downloading　of　the　abstract　is　permitted　for　personal　use　only.