Deep　neural　networks　have　been　widely　applied　across　various　domains,　but　their　numerous　parameters　and　high　computational　demands　limit　their　practical　usage　scenarios.　To　address　this　issue,　this　paper　introduces　a　convolutional　neural　network　compression　method　based　on　multi-factor　channel　pruning.　By　integrating　scaling　and　shifting　factors　from　batch　normalization　layers,　a　multi-factor　channel　salience　metric　is　proposed　to　measure　channel　importance.　By　removing　redundant　channels　within　the　convolutional　neural　network,　a　compressed　model　is　obtained.　On　the　CIFAR-10　dataset,　we　pruned　93.06%　of　the　parameters　and　91.92%　of　the　calculations　from　the　VGG13BN　network,　with　only　a　2.81%　decrease　in　accuracy.　On　the　CIFAR-100　dataset,　we　pruned　72.84%　of　the　parameters　and　72.03%　of　the　calculations　from　the　VGG13BN　network,　with　an　accuracy　improvement　of　4.11%.　©　2023　IEEE.