Online　social　networking　is　prevalent　around　the　world,　meanwhile,　social　bots　damage　users＇　experience　by　stealing　user　privacy,　sending　spam,　spreading　malicious　links,　launching　DDoS　attacks,　et　al.　In　this　paper,　we　use　social　bots　to　automatically　manipulate　social　accounts　based　on　generated　clickstreams　to　demonstrate　the　vulnerability　of　social　networking　sites　and　the　capability　of　social　bots.　Clickstreams　are　the　sequential　click　events　of　users　or　bots　browsing　a　website.　Existing　research　on　clickstream　generation　assumes　that　the　intervals　between　all　click　events　are　equal,　which　makes　it　difficult　to　fully　capture　the　relationship　between　uneven　intervals.　To　this　end,　we　propose　an　uneven　hybrid　clickstream　generation　algorithm　based　on　multi-layer　perceptron　(UHCG-MLP),　which　can　generate　click　events　and　intervals　simultaneously　by　dealing　with　uneven　intervals.　UHCG-MLP　firstly　transforms　each　clickstream　in　the　training　set　into　a　sequence　of　discrete　elements　separated　by　coded　interval,　and　uses　multi-layer　perceptron　to　mine　user　behavior　patterns　from　these　sequences.　Then　it　generates　hybrid　clickstreams　according　to　the　prediction　of　the　trained　model.　Experiments　on　the　Twitter　platform　using　the　clickstreams　generated　by　the　proposed　model　and　the　other　five　benchmark　models　show　that　UHCG-MLP　achieves　the　best　execution　effect.　©　2023　IEEE.