Real-time　monitoring　and　analysis　of　sensitive　areas　in　high-speed　railway　(HSR)　are　crucial　for　ensuring　the　safe　and　smooth　operation　of　high-speed　trains.　To　address　the　problem　of　frequent　missing　and　false　alarms　caused　by　anomaly　data　in　HSR　monitoring　system,　this　study　proposes　an　innovative　network　framework:　Intelligent　detection　network　of　HSR　anomaly　monitoring　data　(HSRA-Net).　The　framework　comprises　of　two　modules:　the　data　augmentation　module　and　the　anomaly　detection　module.　The　data　augmentation　module　designs　multiple　alternative　generative　adversarial　networks　for　sample　augmentation.　To　achieve　the　end-to-end　classification,　the　anomaly　detection　module　improves　the　residual　network　by　creating　a　deep　residual　shrinkage　network　with　self-attention　(DRSN-SA).　An　online　monitoring　system　was　installed　and　operated　continuously　for　several　years　on　a　high-speed　turnout　of　a　continuous　beam　bridge　to　validate　the　proposed　framework.　The　collected　data　includes　displacement,　stress,　and　temperature.　The　proposed　framework　has　demonstrated　excellent　performance,　generalizability,　and　deployability　through　sufficient　model　comparison.　It　can　accurately　and　efficiently　diagnose　anomalies　in　the　operation　of　the　monitoring　system.　This　study　is　of　great　significance　for　improving　the　anomaly　detection　task　of　the　HSR　monitoring　system.