Facial　expression　recognition　is　still　a　problem　at　present,　especially　in　the　case　of　individual　independence.　On　the　one　hand,　due　to　the　influence　of　morphological　changes,　ethnic　differences　and　other　factors,　the　expression　of　individual　expressions　varies　greatly.　On　the　other　hand,　there　is　currently　no　publicly　available　large-scale　dataset　that　can　support　deep　neural　networks.　To　this　end,　this　paper　proposes　cross-connection　and　spatial　pyramid　pooling　convolutional　neural　network.　The　model　not　only　uses　spatial　pyramid　pooling　for　high-level　feature　enhancement,　but　also　combines　cross-connection　and　spatial　pyramid　pooling　to　extract　important　low-level　features.　Finally　the　different　levels　of　features　are　connected　to　improve　the　generalization　performance　of　the　model.　We　validate　our　approach　in　four　widely　used　public　expression　datasets　(CK+,　JAFFE,　MMI,　NimStim).　Compared　to　other　facial　expression　recognition　methods,　our　proposed　method　achieves　comparable　or　superior　results.　In　the　case　of　subject　independence,　the　model　achieved　a　good　result　with　97.41%　accuracy　on　the　CK+　dataset.