In　recent　years,　more　and　more　people　have　been　plagued　by　respiratory　diseases.　The　air　quality,　which　is　characterized　by　inhalable　particles　and　fine　particles,　has　attracted　increasing　attention.　Accurately　monitor　and　forecast　the　quality　of　air　could　not　only　help　the　government　conduct　interventions　to　alleviate　the　air　pollution　earlier,　but　also　alert　relevant　people　who　suffer　from　respiratory　diseases.　In　order　to　develop　effective　Air　Quality　Index　(AQI)　prediction　models,　this　paper　compared　the　performance　of　different　Machine　Learning　(ML)　methods　and　feature　selection　methods.　First　the　air　quality　data　in　Beijing　from　2016　to　2017　were　collected.　Then　Multi-Linear　Regression　(MLR),　Random　forest　Regression　(RFR),　BP　Neural　Network　(BPNN)　and　Support　Vector　Regression　(SVR)　algorithm　were　trained　on　10-fold　cross　validation.　Correlation　coefficient　(R),　mean　absolute　error　(MAE)　and　root　mean　square　error　(RMSE)　were　used　as　evaluation　metrics.　The　experimental　results　showed　that　the　performance　of　SVR　and　BPNN　were　similarly　well.　MLR　had　the　worst　performance,　which　was　possibly　caused　by　a　small　feature　dimension,　and　RFR　had　higher　accuracy　and　better　generalization　capability　than　the　other　models,　probably　because　the　algorithm　of　regression　tree　in　random　forest　included　the　interaction　of　variables.　©　2019,　Springer　Nature　Singapore　Pte　Ltd.