Increasingly,　more　people　are　suffering　from　the　effects　of　air　pollution.　This　study　took　Beijing　as　an　example　and　proposed　an　attention-based　air　quality　predictor　(AAQP)　that　could　better　protect　people　from　air　pollution.　The　AAQP　is　a　seq2seq　model,　and　it　exploits　historical　air　quality　data　and　weather　data　to　predict　future　air　quality　indexes.　Although　existing　research　has　promoted　seq2seq　for　air　quality　prediction,　there　are　still　two　problems.　First,　the　seq2seq　has　a　slow　training　speed　so　the　original　RNN　in　the　encoder　was　replaced　with　a　fully　connected　encoder　to　accelerate　the　training　process.　Position　embedding　was　also　introduced　to　help　the　fully　connected　encoder　find　the　sequential　relationships　among　source　sequences.　Another　problem　is　error　accumulation　caused　by　recurrent　prediction.　Accordingly,　the　n-step　recurrent　prediction　was　proposed　to　solve　this　problem.　The　experimental　results　validated　that　the　AAQP　with　n-step　recurrent　prediction　had　better　performance　than　the　related　arts　since　the　error　accumulation　was　reduced,　and　the　training　time　was　significantly　decreased　compared　with　the　original　seq2seq　attention　model.