Long　time　exposure　to　indoor　air　pollution　environments　can　increase　the　risk　of　cardiovascular　and　respiratory　system　damage.　Most　previous　studies　focus　on　outdoor　air　quality,　while　few　studies　on　indoor　air　quality.　Current　neural　network-based　methods　for　indoor　air　quality　prediction　ignore　the　optimization　of　input　variables,　process　input　features　serially,　and　still　suffer　from　loss　of　information　during　model　training,　which　may　lead　to　the　problems　of　memory-intensive,　time-consuming　and　low-precision.　We　present　a　novel　concurrent　indoor　PM　prediction　model　based　on　the　fusion　model　of　Least　Absolute　Shrinkage　and　Selection　Operator　(LASSO)　and　an　Attention　Temporal　Convolutional　Network　(ATCN),　together　called　LATCN.　First,　a　LASSO　regression　algorithm　is　used　to　select　features　from　PM1,　PM2.5,　PM10　and　PM　(＞10)　datasets　and　environmental　factors　to　optimize　the　inputs　for　indoor　PM　prediction　model.　Then　an　Attention　Mechanism　(AM)　is　applied　to　reduce　the　redundant　temporal　information　to　extract　key　features　in　inputs.　Finally,　a　TCN　is　used　to　forecast　indoor　particulate　concentration　in　parallel　with　inputting　the　extracted　features,　and　it　reduces　information　loss　by　residual　connections.　The　results　show　that　the　main　environmental　factors　affecting　indoor　PM　concentration　are　the　indoor　heat　index,　indoor　wind　chill,　wet　bulb　temperature　and　relative　humidity.　Comparing　with　Long　Short-Term　Memory　(LSTM)　and　Gated　Recurrent　Unit　(GRU)　approaches,　LATCN　systematically　reduced　the　prediction　error　rate　(19.7%　similar　to　28.1%　for　the　NAE,　and　16.4%　similar　to　21.5%　for　the　RMSE)　and　improved　themodel　running　speed　(30.4%　similar　to　81.2%)　over　these　classical　sequence　prediction　models.　Our　study　can　inform　the　active　prevention　of　indoor　air　pollution,　and　provides　a　theoretical　basis　for　indoor　environmental　standards,　while　laying　the　foundations　for　developing　novel　air　pollution　prevention　equipment　in　the　future.