Affective　computing　is　an　increasingly　important　outgrowth　of　Artificial　Intelligence,　which　is　intended　to　deal　with　rich　and　subjective　human　communication.　In　view　of　the　complexity　of　affective　expression,　discriminative　feature　extraction　and　corresponding　high-performance　classifier　selection　are　still　a　big　challenge.　Specific　features/classifiers　display　different　performance　in　different　datasets.　There　has　currently　been　no　consensus　in　the　literature　that　any　expression　feature　or　classifier　is　always　good　in　all　cases.　Although　the　recently　updated　deep　learning　algorithm,　which　uses　learning　deep　feature　instead　of　manual　construction,　appears　in　the　expression　recognition　research,　the　limitation　of　training　samples　is　still　an　obstacle　of　practical　application.　In　this　paper,　we　aim　to　find　an　effective　solution　based　on　a　fusion　and　association　learning　strategy　with　typical　manual　features　and　classifiers.　Taking　these　typical　features　and　classifiers　in　facial　expression　area　as　a　basis,　we　fully　analyse　their　fusion　performance.　Meanwhile,　to　emphasize　the　major　attributions　of　affective　computing,　we　select　facial　expression　relative　Action　Units　(AUs)　as　basic　components.　In　addition,　we　employ　association　rules　to　mine　the　relationships　between　AUs　and　facial　expressions.　Based　on　a　comprehensive　analysis　from　different　perspectives,　we　propose　a　novel　facial　expression　recognition　approach　that　uses　multiple　features　and　multiple　classifiers　embedded　into　a　stacking　framework　based　on　AUs.　Extensive　experiments　on　two　public　datasets　show　that　our　proposed　multi-layer　fusion　system　based　on　optimal　AUs　weighting　has　gained　dramatic　improvements　on　facial　expression　recognition　in　comparison　to　an　individual　feature/classifier　and　some　state-of-the-art　methods,　including　the　recent　deep　learning　based　expression　recognition　one.