Air　pollution　is　an　environmental　problem　facing　mankind　today.　Therefore,　predicting　the　concentration　of　air　pollutants　in　advance　plays　an　important　role　in　people＇s　life　and　government　decision-making.　In　this　paper,　a　multi-channel　asymmetric　structure　prediction　model　based　on　temporal　convolutional　network　(TCN)　is　proposed.　As　TCN　omits　some　feature　information　when　learning　time　series　features,　increasing　the　number　of　channels　will　improve　the　receptive　field　of　the　model,　cover　longer　historical　information　and　extract　more　time　series　features.　The　influence　of　meteorological　factors　on　the　concentration　of　air　pollutants　is　fully　considered　in　the　prediction　model,　which　is　used　as　an　auxiliary　factor　to　improve　the　prediction　performance　of　the　model.　The　concentration　of　air　pollutants　collected　from　the　air　monitoring　station　in　Fushun　City,　Liaoning　Province,　is　used　as　the　data　set　to　verify　the　effectiveness　of　the　model,　and　the　experimental　comparison　with　other　prediction　models　is　conducted.　The　results　show　that　the　model　proposed　in　this　paper　has　more　accurate　prediction　accuracy　and　stronger　stability.　©　2022　SPIE.