Contrastive　learning　is　an　increasingly　important　research　direction　and　has　attracted　considerable　attention　in　the　field　of　computer　vision.　It　can　greatly　improve　the　representativeness　of　image　features　through　data　augmentation,　unsupervised　learning,　and　pre-trained　models.　However,　in　the　field　of　traffic　flow　forecasting,　most　graph-based　models　focus　on　the　construct　of　spatial-temporal　relationships　between　road　segments　and　ignore　the　use　of　temporal　data　augmentation　and　pre-trained　models,　which　can　improve　the　representation　ability　of　the　forecasting　model.　Therefore,　in　this　work,　contrastive　learning　are　used　to　expand　the　distribution　of　sequence　samples　and　improve　the　quality　and　generalization　of　forecasting　models.　Based　on　this,　a　novel　forecasting　model　called　contrastive　learning　based　on　multi　graph　convolution　network　(CLMGCN)　is　proposed,　which　is　combined　with　four　components:　multi　graph　convolution　network,　which　learns　the　spatial-temporal　feature　of　the　input　traffic　data;　temporal　data　augmentation,　which　obtains　the　augmentation　data　of　the　input　traffic　data;　contrastive　learning,　which　achieves　the　pre-training　phase　and　improve　the　quality　of　output　feature　of　multi　graph　convolution　network;　output　block,　which　utilizes　the　enhanced　output　feature　of　multi　graph　convolution　network　for　predicting　the　future　traffic　data.　Finally,　by　the　experimental　results　of　four　public　traffic　flow　datasets,　it　can　be　shown　that　CLMGCN　achieves　higher　traffic　forecasting　accuracy　with　lower　model　complexity.　we　propose　a　novel　forecasting　model　called　contrastive　learning　based　on　multi　graph　convolution　network　(CLMGCN).　By　the　experimental　results　of　four　public　traffic　flow　datasets,　it　can　be　shown　that　CLMGCN　achieves　higher　traffic　forecasting　accuracy　with　lower　model　complexity.image