Accurate　and　fine-grained　individual　air　quality　index　(IAQI)　prediction　is　the　basis　of　air　quality　index　(AQI),　which　is　of　great　significance　for　air　quality　control　and　human　health.　Traditional　approaches,　such　as　time　series,　recurrent　neural　network　or　graph　convolutional　network,　cannot　effectively　integrate　spatial-temporal　and　meteorological　factors　and　manage　the　dynamic　edge　relationship　among　scattered　monitoring　stations.　In　this　paper,　a　ST-CCN-IAQI　model　is　proposed　based　on　spatial-temporal　causal　convolution　networks.　Both　the　spatial　effects　of　multi-source　air　pollutants　and　meteorological　factors　were　considered　via　spatial　attention　mechanism.　Time-dependent　features　in　the　causal　convolution　network　were　extracted　by　stacked　dilated　convolution　and　time　attention.　All　the　hyper-parameters　in　ST-CCN-IAQI　were　tuned　by　Bayesian　optimization.　Shanghai　air　monitoring　station　data　were　employed　with　a　series　of　baselines　(AR,　MA,　ARMA,　ANN,　SVR,　GRU,　LSTM　and　ST-GCN).　Final　results　showed　that:　(1)　For　a　single　station,　the　RMSE　and　MAE　values　of　ST-CCN-IAQI　were　9.873　and　7.469,　decreasing　by　24.95%　and　16.87%　on　average,　respectively.　R-2　was　0.917,　with　an　average　5.69%　improvement;　(2)　For　all　nine　stations,　the　mean　RMSE　and　MAE　of　ST-CCN-IAQI　were　9.849　and　7.527,　respectively,　and　the　R-2　value　was　0.906.　(3)　Shapley　analysis　showed　PM10,　humidity　and　NO2　were　the　most　influencing　factors　in　ST-CCN-IAQI.　The　Friedman　test,　under　different　resampling,　further　confirmed　the　advantage　of　ST-CCN-IAQI.　The　ST-CCN-IAQI　provides　a　promising　direction　for　fine-grained　IAQI　prediction.