Facial　expression　recognition　has　always　been　a　focus　of　study　in　emotion　computing.　With　the　aim　of　solving　the　problem　that　previous　facial　expression　recognition　studies　are　susceptible　to　individual　differences　and　background　noise,　as　well　as　the　loss　of　facial　expression　features　in　deep　networks　of　deep　convolutional　networks,　we　propose　a　facial　expression　method　based　on　convolutional　difference　contextual　feature　fusion.　The　convolutional　difference　module　can　carry　out　difference　operation　between　emotional　image　and　neutral　image　in　the　input　image,　eliminate　individual　difference　and　background　noise　in　expression　features　and　improve　the　accuracy　and　generalization　of　the　network.　Finally,　convolutional　difference　and　contextual　features　are　combined　to　improve　the　accuracy　of　the　network　further.　Experiment　results　of　4　public　facial　expression　datasets　illustrate　that　compared　with　the　existing　facial　expression　recognition　methods,　this　method　has　higher　accuracy,　can　extract　facial　expression　features　more　effectively,　and　has　a　better　generalization　ability.　©　2023　SPIE.