Video content classification is an important research content in computer vision, which is widely used in many fields, such as image and video retrieval, computer vision. This paper presents a model that is a combination of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) which develops, trains, and optimizes a deep learning network that can identify the type of video content and classify them into categories such as "Animation, Gaming, natural content, flat content, etc". To enhance the performance of the model novel keyframe extraction method is included to classify only the keyframes, thereby reducing the overall processing time without sacrificing any significant performance.

翻译：视频内容分类是计算机视觉中的一项重要研究内容,在图像和视频检索、计算机视觉等许多领域广泛使用,本文展示了一种模式,结合了进化神经网络和经常性神经网络,开发、培训和优化深层学习网络,可以识别视频内容的类型,并将其分为“动画、赌博、自然内容、平板内容等”类别。 为了提高新型新型键盘提取模型的性能,只对关键框架进行分类,从而缩短总体处理时间,而不会牺牲任何显著的性能。