In　daily　life,　expression　recognition　is　often　used　in　medicine,　transportation,　education　and　other　fields.　In　this　paper,　a　multi-scale　fusion　face　expression　recognition　algorithm　based　on　VGGNet19　is　proposed　to　address　the　problem　of　low　recognition　rate　that　occurs　in　existing　convolutional　neural　networks　for　face　expression　recognition.　The　improved　Xception　network　architecture　is　fused　with　VGGNet19　to　improve　the　feature　extraction　capability.　In　addition,　for　VGGNet19,　only　one　fully-connected　layer　is　retained　to　reduce　the　number　of　parameters　and　improve　computer　efficiency,　and　a　dropout　strategy　is　introduced　before　the　fully-connected　layer　to　prevent　overfitting,　and　finally　the　cross-entropy　function　is　combined　with　Softmax　for　classification　recognition.　The　final　cross-entropy　function　was　combined　with　Softmax　for　classification　and　finally　obtained　an　accuracy　of　89.83%　on　the　RAF-DB　dataset.　©　2023　IEEE.