Name　disambiguation　has　long　been　a　significant　issue　in　many　fields,　such　as　literature　management　and　social　analysis.　In　recent　years,　methods　based　on　graph　networks　have　performed　well　in　name　disambiguation,　but　these　works　have　rarely　used　heterogeneous　graphs　to　capture　relationships　between　nodes.　Heterogeneous　graphs　can　extract　more　comprehensive　relationship　information　so　that　more　accurate　node　embedding　can　be　learned.　Therefore,　a　Dual-Channel　Heterogeneous　Graph　Network　is　proposed　to　solve　the　name　disambiguation　problem.　We　use　the　heterogeneous　graph　network　to　capture　various　node　information　to　ensure　that　our　method　can　learn　more　accurate　data　structure　information.　In　addition,　we　use　fastText　to　extract　the　semantic　information　of　the　data.　Then,　a　clustering　method　based　on　DBSCAN　is　used　to　classify　academic　papers　by　different　authors　into　different　clusters.　In　many　experiments　based　on　real　datasets,　our　method　achieved　high　accuracy,　which　proves　its　effectiveness.　©　2021　by　the　authors.　Licensee　MDPI,　Basel,　Switzerland.