A video type detection method, comprising: obtaining N key frames of a first video, wherein N is an integer greater than 1, and a type of the first video is to be detected; obtaining M confidence scores corresponding to each of the N key frames by inputting each of the N key frames into M algorithm models corresponding to a first video type respectively, wherein M is an integer greater than 1, and the M algorithm models comprise: a classification algorithm model, a feature logo algorithm model and a feature person algorithm model, for each key frame, the classification algorithm model is used to output a confidence score which is positively correlated with a probability of elements corresponding to the first video type contained in a key frame, the feature logo algorithm model is used to output a confidence score which is positively correlated with a probability of a logo corresponding to the first video type contained in the key frame, and the feature person algorithm model is used to output a confidence score which is positively correlated with a probability of a person corresponding to the first video type contained in the key frame, and the first video type comprises one of the following: a violent and terrorist video type, a political video type and a prohibited video type; determining a confidence score of the first video by a fusion strategy algorithm model according to N×M confidence scores of the N key frames; and comparing the confidence score of the first video with a confidence score threshold corresponding to the first video type, to determine whether the type of the first video is the first video type or not. obtaining N key frames of a first video, wherein N is an integer greater than 1, and a type of the first video is to be detected; obtaining M confidence scores corresponding to each of the N key frames by inputting each of the N key frames into M algorithm models corresponding to a first video type respectively, wherein M is an integer greater than 1, and the M algorithm models comprise: a classification algorithm model, a feature logo algorithm model and a feature person algorithm model, for each key frame, the classification algorithm model is used to output a confidence score which is positively correlated with a probability of elements corresponding to the first video type contained in a key frame, the feature logo algorithm model is used to output a confidence score which is positively correlated with a probability of a logo corresponding to the first video type contained in the key frame, and the feature person algorithm model is used to output a confidence score which is positively correlated with a probability of a person corresponding to the first video type contained in the key frame, and the first video type comprises one of the following: a violent and terrorist video type, a political video type and a prohibited video type; determining a confidence score of the first video by a fusion strategy algorithm model according to N×M confidence scores of the N key frames; and comparing the confidence score of the first video with a confidence score threshold corresponding to the first video type, to determine whether the type of the first video is the first video type or not.