Efficient　and　accurate　identification　of　network　anomalies　is　significant　to　network　security　systems.　It　is　highly　challenging　to　detect　abnormal　behaviors　in　the　increasing　network　data　accurately.　Currently,　classification　methods　based　on　feature　extraction　of　autoencoders　have　been　proven　to　be　suitable　for　network　anomaly　detection.　However,　traditional　detection　models　with　autoencoders　have　unsatisfying　detection　accuracy　in　the　face　of　massive　network　features.　In　addition,　the　hyperparameter　optimization　of　their　models　cannot　be　effectively　solved.　In　this　work,　based　on　the　improvement　of　variational　autoencoders,　stacked　sparse　shrink　variational　autoencoders　(S3VAEs)　are　designed.　In　addition,　an　Unbalanced　XGBoost　classifier　based　on　Genetic　simulated　annealing　particle　swarm　optimization　(UXG)　is　proposed.　Finally,　the　feature　extractor　of　S3VAEs　is　combined　with　the　UXG　classifier,　and　the　anomaly　detection　model　is　obtained.　Experimental　results　based　on　four　real-life　data　sets　demonstrate　that　the　proposed　anomaly　detection　model　achieves　higher　classification　accuracy　and　F1　than　several　state-of-the-art　algorithms.　IEEE