Structured　pruning　is　a　representative　model　compression　technology　for　convolutional　neural　networks　(CNNs),　aiming　to　prune　some　less　important　filters　or　channels　of　CNNs.　Most　recent　structured　pruning　methods　have　established　some　criteria　to　measure　the　importance　of　filters,　which　are　mainly　based　on　the　magnitude　of　weights　or　other　parameters　in　CNNs.　However,　these　judgment　criteria　lack　explainability,　and　it　is　insufficient　to　simply　rely　on　the　numerical　values　of　the　network　parameters　to　assess　the　relationship　between　the　channel　and　the　model　performance.　Moreover,　directly　utilizing　these　pruning　criteria　for　global　pruning　may　lead　to　suboptimal　solutions,　therefore,　it　is　necessary　to　complement　search　algorithms　to　determine　the　pruning　ratio　for　each　layer.　To　address　these　issues,　we　propose　ARPruning　(Attention　-map　-based　Ranking　Pruning),　which　reconstructs　a　new　pruning　criterion　as　the　importance　of　the　intra-layer　channels　and　further　develops　a　new　local　neighborhood　search　algorithm　for　determining　the　optimal　inter　-layer　pruning　ratio.　To　measure　the　relationship　between　the　channel　to　be　pruned　and　the　model　performance,　we　construct　an　intra-layer　channel　importance　criterion　by　considering　the　attention　map　for　each　layer.　Then,　we　propose　an　automatic　pruning　strategy　searching　method　that　can　search　for　the　optimal　solution　effectively　and　efficiently.　By　integrating　the　well　-designed　pruning　criteria　and　search　strategy,　our　ARPruning　can　not　only　maintain　high　compression　rate　but　also　achieve　outstanding　accuracy.　In　our　work,　it　is　also　experimentally　concluded　that　compared　with　state-of-the-art　pruning　methods,　our　ARPruning　method　is　capable　of　achieving　better　compression　results.　The　code　can　be　obtained　at　https://github.com/dozingLee/ARPruning.