Automatically　detecting　anomalous　events　in　surveillance　videos　is　crucial　for　security　maintenance.　Due　to　the　challenging　nature　of　the　task,　the　performance　of　the　existing　approaches　is　still　limited.　In　this　study,　we　propose　a　video　anomaly　detection　method　called　multi-scale　Siamese　prediction　framework　(MSSP),　where　the　Siamese　network　uses　the　information　embedded　in　the　observed　anomalous　events　without　requiring　any　additional　parameters.　To　extract　spatiotemporal　features,　we　introduce　a　multi-scale　term　where　an　improved　inception　module　and　a　convolutional　GRU　(Conv-GRU)　module　are　combined.　They　are　employed　in　each　layer　of　the　U-Net　coding　stage　to　mitigate　the　information　loss　caused　by　subsampling.　To　further　optimize　the　proposed　model,　a　loss　function　combining　the　prediction　loss　function　and　the　contrastive　loss　is　proposed.　We　evaluate　the　system　performance　on　three　public　datasets:　CUHK　Avenue,　UCSD　Ped2,　and　ShanghaiTech　dataset.　Experimental　results　demonstrated　that　the　MSSP　framework　achieved　AUC　values　of　89.4%,　97.4%　and　73.83%,　respectively,　which　significantly　outperforms　other　methods.