It　has　been　gradually　recognized　that　mobile　phones　can　be　used　as　a　practical　and　promising　way　to　identify　individual　travel　trajectories.　Researchers　have　developed　various　approaches　to　detecting　human　mobility　and　trip　characteristics　including　trip　origin-destination,　travel　modes,　trip　purposes　based　on　mobile　phone　data.　Among　these　researches,　trip　purpose　detection　has　drawn　less　attention　from　researchers.　This　paper　presents　our　work　to　investigate　a　set　of　machine　learning　approaches　to　identifying　the　trip　purposes　for　tourists　based　on　mobile　signaling　data　combined　with　sampling　surveys　and　point　of　interest　(POI)　data.　Five　machine　learning　algorithms,　including　support　vector　machine,　decision　tree,　random　forest,　artificial　neural　network,　and　deep　stacked　auto-encoded　(DSAE),　have　been　employed　to　infer　trip　purposes　under　multiple　scenarios.　Four　scenarios　have　been　designed　by　considering　the　POI　information　around　trip　end　[a　500　m　buffer　or　Thiessen　polygon　(the　coverage　of　the　base　station　theoretically)]　and　training　dataset　selection　(equal　probabilities　selection　or　equal　proportion　selection).　The　accuracy　of　trip　purpose　classification　with　machine　learning　algorithms　has　compared　under　different　scenarios.　The　highest　accuracy　of　93.47%　for　the　test　dataset　is　achieved　based　on　DSAE　model　under　the　scenario　of　a　trip　end　500　m　buffer　and　equal　probabilities　selection.　The　experimental　results　indicate　that　the　methodology　developed　with　machine　learning　algorithms　based　on　mobile　signaling　data　combined　with　sample　travel　survey　is　expected　as　an　alternative　way　to　traditional　travel　surveys　for　trip　purposes.