The　attribute　extraction　process　can　be　separated　into　two　phases,　alignment　and　annotation.　In　the　existing　alignment　methods,　different　semantic　attributes　are　mistakenly　aligned　into　the　same　group.　Furthermore,　to　improve　the　accuracy　of　semantic　annotation,　time-consuming　manual　annotation　is　often　introduced　to　construct　training　set.　To　solve　this　problem,　a　multi-record　webpage　attribute　extraction　method　combining　active　learning　is　presented.　As　for　the　problem　of　wrong　attribute　alignment,　shallow　semantic　is　integrated　into　the　alignment　approach　to　relieve　the　influence　of　same　tags　with　different　semantics.　In　the　semantic　annotation　phase,　textual,　visual　and　global　features　are　extracted　for　semantic　classification　and　an　active　learning　based　SVM　classifier　is　applied　to　extract　structural　data.　Moreover,　a　new　sample　selection　strategy　is　proposed　by　introducing　the　global　sample　information,　and　more　informative　samples　with　lower　confidences　are　selected　to　be　labeled.　The　experimental　results　on　BBS　and　microblog　datasets　confirm　the　superiority　the　proposed　method.　©　2016,　Science　Press.　All　right　reserved.