Malicious　social　robots　are　the　disseminators　of　malicious　information　on　social　networks,　which　seriously　affect　information　security　and　network　environments.　Efficient　and　reliable　classification　of　social　robots　is　crucial　for　detecting　information　manipulation　in　social　networks.　Supervised　classification　based　on　manual　feature　extraction　has　been　widely　used　in　social　robot　detection.　However,　these　methods　not　only　involve　the　privacy　of　users　but　also　ignore　hidden　feature　information,　especially　the　graph　feature,　and　the　label　utilization　rate　of　semi-supervised　algorithms　is　low.　Aiming　at　the　problems　of　shallow　feature　extraction　and　low　label　utilization　rate　in　existing　social　network　robot　detection　methods,　in　this　paper　a　robot　detection　scheme　based　on　weighted　network　topology　is　proposed,　which　introduces　an　improved　network　representation　learning　algorithm　to　extract　the　local　structure　features　of　the　network,　and　combined　with　the　graph　convolution　network　(GCN)　algorithm　based　on　the　graph　filter,　to　obtain　the　global　structure　features　of　the　network.　An　end-to-end　semi-supervised　combination　model　(Semi-GSGCN)　is　established　to　detect　malicious　social　robots.　Experiments　on　a　social　network　dataset　(cresci-rtbust-2019)　show　that　the　proposed　method　has　high　versatility　and　effectiveness　in　detecting　social　robots.　In　addition,　this　method　has　a　stronger　insight　into　robots　in　social　networks　than　other　methods.