The　long-tailed　distribution　of　data　poses　a　significant　challenge　in　text　classification　tasks.　The　imbalanced　distribution　of　samples　among　categories　often　hinders　effective　classification　of　categories　with　a　limited　number　of　samples　(referred　to　as　tail　class),　leading　to　suboptimal　overall　classification　performance.　To　address　this　issue　and　accurately　classify　the　texts　of　complaints　and　reports,　this　paper　proposes　a　text　classification　method　tailored　for　long-tailed　distribution,　leveraging　a　rebalanced　loss　function　to　adjust　the　weights　of　samples　from　different　categories.　In　the　proposed　approach,　we　first　improve　the　classic　loss　function　by　incorporating　the　Gumbel　activation　function　to　replace　the　conventional　activation　function.　This　modification　imparts　varying　gradients　to　both　head　class　and　tail　class,　thereby　mitigating　classification　bias.　Subsequently,　to　counteract　overfitting　in　the　tail　class,　regularization　constraints　is　introduced　within　the　loss　function,　enhancing　its　generalization　capability.　Experimental　results　demonstrate　that,　when　compared　to　alternative　loss　functions,　the　method　presented　in　this　paper　yields　superior　classification　results　in　addressing　multi-classification　problems　characterized　by　long-tailed　distribution　of　text　data.　©　2024　IEEE.