Video　anomaly　detection　refers　to　detecting　and　recognising　abnormal　performance　in　videos　that　deviate　from　normal　behaviour.　The　anomaly　detection　performance　in　weakly　supervised　video　anomaly　detection　degrades　due　to　the　lack　of　attention　to　temporal　information　in　the　video　features　extracted　by　the　pre-trained　network.　To　address　this　problem,　we　propose　a　weakly　supervised　video　anomaly　detection　method　based　on　a　self-attention　pyramidal　convolutional　network　(SAP-net),　which　includes　a　redesigned　multi-scale　module　with　a　self-attention　mechanism.　Experimental　results　show　the　SAP-net　outperforms　the　state-of-the-art　method　in　the　UCF-Crime　dataset.　©　2022　Association　for　Computing　Machinery.