To　achieve　fake　complaint　reporting　detection,　we　propose　a　false　complaint　detection　model　based　on　contrastive　learning.　Firstly,　the　pre-trained　model　is　used　as　a　feature　extractor　to　extract　the　complaint　reporting　sample　features;　secondly,　the　sample　features　of　different　categories　are　compared　and　learned　to　reconstruct　the　feature　distribution　in　the　feature　space;　finally,　the　classifier　outputs　the　classification　result　and　the　network　parameters　are　updated　by　the　contrastive　loss　and　balance　loss　together.　In　this　paper,　we　designed　comparison　experiments　and　ablation　experiments,　and　the　F1　and　AUC　values　of　the　model　reached　81.40%　and　81.46%,　indicating　that　the　contrastive　learning　model　has　better　performance　and　is　suitable　to　be　applied　in　the　fake　complaint　detection　task.　©　2023　IEEE.