The　rapid　detection　of　the　novel　coronavirus　disease,　COVID-19,　has　a　positive　effect　on　preventing　propagation　and　enhancing　therapeutic　outcomes.　This　article　focuses　on　the　rapid　detection　of　COVID-19.　We　propose　an　ensemble　deep　learning　model　for　novel　COVID-19　detection　from　CT　images.　2933　lung　CT　images　from　COVID-19　patients　were　obtained　from　previous　publications,　authoritative　media　reports,　and　public　databases.　The　images　were　preprocessed　to　obtain　2500　high-quality　images.　2500　CT　images　of　lung　tumor　and　2500　from　normal　lung　were　obtained　from　a　hospital.　Transfer　learning　was　used　to　initialize　model　parameters　and　pretrain　three　deep　convolutional　neural　network　models:　AlexNet,　GoogleNet,　and　ResNet.　These　models　were　used　for　feature　extraction　on　all　images.　Softmax　was　used　as　the　classification　algorithm　of　the　fully　connected　layer.　The　ensemble　classifier　EDL-COVID　was　obtained　via　relative　majority　voting.　Finally,　the　ensemble　classifier　was　compared　with　three　component　classifiers　to　evaluate　accuracy,　sensitivity,　specificity,　F　value,　and　Matthews　correlation　coefficient.　The　results　showed　that　the　overall　classification　performance　of　the　ensemble　model　was　better　than　that　of　the　component　classifier.　The　evaluation　indexes　were　also　higher.　This　algorithm　can　better　meet　the　rapid　detection　requirements　of　the　novel　coronavirus　disease　COVID-19.