Age,　gender,　educational　background,　and　so　on　are　the　most　basic　attributes　for　identifying　and　portraying　users.　It　is　also　possible　to　conduct　in-depth　mining　analysis　and　high-level　predictions　based　on　such　attributes　to　learn　users’　preferences　and　personalities　so　as　to　enhance　users’　online　experience　and　to　realize　personalized　services　in　real　applications.　In　this　paper,　we　propose　using　classification　algorithms　in　machine　learning　to　predict　users’　demographic　attributes,　such　as　gender,　age,　and　educational　background,　based　on　one　month　of　data　collected　with　the　Sogou　search　engine　with　the　goal　of　making　user　portraits.　A　multi-model　approach　using　the　fusion　algorithms　is　adopted　and　hereby　described　in　the　paper.　The　proposed　model　is　a　two-stage　structure　using　one　month　of　data　with　demographic　labels　as　the　training　data.　The　first　stage　of　the　structure　is　based　on　traditional　machine　learning　models　and　neural　network　models,　whereas　the　second　one　is　a　combination　of　the　models　from　the　first　stage.　Experimental　results　show　that　our　proposed　multi-model　method　can　achieve　more　accurate　results　than　the　single-model　methods　in　predicting　user　attributes.　The　proposed　approach　also　has　stronger　generalization　ability　in　predicting　users’　demographic　attributes,　making　it　more　adequate　to　profile　users.　©　2021　by　the　authors.　Licensee　MDPI,　Basel,　Switzerland.