Air　pollution　has　been　plaguing　human　society　and　seriously　affects　the　normal　activities　of　human　beings.　PM2.5　is　a　kind　of　air　suspension　particles　with　complex　composition,　which　is　one　of　the　main　components　of　air　pollution　and　poses　serious　threat　to　human　health.　In　this　paper,　a　model　based　Temporal　Convolution　Network　(TCN)　was　applied　to　predict　outdoor　PM2.5　in　Beijing.　Model　based　on　TCN　is　capable　to　parallel　operations　and　has　flexible　convolution　kernel　compared　with　models　based　on　other　neural　networks　such　as　Recurrent　Neural　Network　(RNN),　Long　Short-Term　Memory　(LSTM),　Gate　Recurrent　Unit　(GRU)　and　traditional　Convolutional　Neural　Network　(CNN).　Additionally,　the　model　was　verified　in　the　experiment　and　the　results　indicated　that　the　model　achieved　better　performance　than　traditional　models.