The　evaluations　of　traditional　teaching　quality　are　mainly　subjective,　and　there　is　a　lack　of　fine-grained　objective　data　to　support　the　evaluation　of　teaching　states　in　the　classroom.　In　this　paper,　an　intensity-based　facial　expression　dataset　is　proposed　and　named　EIDB-13,　which　contains　13　kinds　and　10393　facial　images　collected　from　thousands　of　individuals　and　existing　facial　expression　datasets.　Convolutional　neural　network　(CNN)　and　attention　mechanism　are　combined　to　recognize　facial　expressions.　Migration　learning　is　used　to　solve　over-fitting　problem　in　the　process　of　training　deep　network　based　on　the　small　sample　dataset.　InceptionResNetV2　is　employed　as　migration　network.　Furthermore,　an　InceptionResNetV2+CBAM　network　proposed　extract　similar　feature　information　among　facial　expressions　and　it　outperforms　the　network　without　attention　mechanisms.　Experiments　show　a　classification　accuracy　rate　of　78%　on　the　intensity-based　facial　expression　dataset　EIDB-13　and　of　88%　on　the　public　macro　expression　dataset　RAF-DB.　Combining　facial　expression　recognition　technology　into　teaching　is　a　key　foundation　to　study　teaching　quality　on　the　intensity　of　teacher＇s　expression.