The　rapid　deployment　and　low-cost　inference　of　controller　area　network　(CAN)　bus　anomaly　detection　models　on　intelligent　vehicles　can　drive　the　development　of　the　Green　Internet　of　Vehicles.　Anomaly　detection　on　intelligent　vehicles　often　utilizes　recurrent　neural　network　models,　but　computational　resources　for　these　models　are　limited　on　small　platforms.　Model　compression　is　essential　to　ensure　CAN　bus　security　with　restricted　computing　resources　while　improving　model　computation　efficiency.　However,　the　existence　of　shared　cyclic　units　significantly　constrains　the　compression　of　recurrent　neural　networks.　In　this　study,　we　propose　a　structured　pruning　method　for　long　short-term　memory　(LSTM),　based　on　the　contribution　values　of　shared　vectors.　By　analyzing　the　contribution　value　of　each　dimension　of　shared　vectors,　the　weight　matrix　of　the　model　is　structurally　pruned,　and　the　output　value　of　the　LSTM　layer　is　supplemented,　to　maintain　the　information　integrity　between　adjacent　network　layers.　We　further　propose　an　approximate　matrix　multiplication　calculation　module　that　runs　in　the　whole　process　of　model　calculation　and　is　deployed　in　parallel　with　the　pruning　module.　Evaluated　on　a　realistic　public　CAN　bus　dataset,　our　method　effectively　achieves　highly　structured　pruning,　improves　model　computing　efficiency,　and　maintains　performance　stability　compared　to　other　compression　methods.　IEEE