Malicious　social　media　bots　are　disseminators　of　malicious　information　on　social　networks　and　seriously　affect　information　security　and　the　network　environment.　Efficient　and　reliable　classification　of　social　media　bots　is　crucial　for　detecting　information　manipulation　in　social　networks.　Aiming　to　correct　the　defects　of　high-cost　labeling　and　unbalanced　positive　and　negative　samples　in　the　existing　methods　of　social　media　bot　detection,　and　to　reduce　the　training　of　abnormal　samples　in　the　model,　we　propose　an　anomaly　detection　framework　based　on　a　combination　of　a　Variational　AutoEncoder　and　an　anomaly　detection　algorithm.　The　purpose　is　to　use　Variational　AutoEncoder　to　automatically　encode　and　decode　sample　features.　The　normal　sample　features　are　more　similar　to　the　initial　features　after　decoding;　however,　there　is　a　difference　between　the　abnormal　samples　and　the　initial　features.　The　decoding　representation　and　the　original　features　are　combined,　and　then　the　anomaly　detection　method　is　used　for　detection.　The　results　show　that　the　area　under　the　curve　of　the　proposed　model　for　identifying　social　media　bots　reaches　98%　through　the　experiments　on　public　datasets,　which　can　effectively　distinguish　bots　from　common　users　and　further　verify　the　performance　of　the　proposed　model.