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Abstract: The diversity and complexity of video content make video label classification difficult. Different videos may have similar features but belong to different categories, or videos of the same category may have significant differences in presentation. To effectively improve the accuracy of video label classification results, a video label classification algorithm combining CNN(Convolutional Neural Network) and rotated forest is proposed. Classify film and video tags into two stages. In the first stage, the rotation forest algorithm is used to segment the sample set of film and television video labels. Through feature transformation, each subset of samples is transformed into a completely new feature space, and multiple new sample subsets with significant differences are obtained. The AdaBoost algorithm is used to iterate multiple times in the sample set and construct multiple AdaBoost classifiers.The probability averaging method is introduced to fuse the classification results and obtain preliminary label classification results. In the second stage, the film and television video features captured by the quaternion Gabor filtering convolution algorithm and the preliminary classification results of the labels obtained in the first stage are used as inputs for the CNN. L1 regularization is introduced in the fully connected layer to constrain the complexity of the model and prevent overfitting. The film and television video label classification is completed through multiple rounds of iterative training. The test results show that the proposed algorithm has good performance in film and television video label classification and can effectively meet the personalized needs of users.

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