Design and implementation of short video multi-labels generating based on content-aware dynamic graph convolutional networks

In recent years, short video platforms have gained prominence as a primary medium for communication. This paper delves into their application within the electric power industry. User-generated labels often fall short of effectively matching video content, providing limited guidance to both content reviewers and viewers. Moreover, manual content review heavily relies on human resources, necessitating a more intelligent approach to label generation. To address this, the paper introduces a Content-Aware Dynamic Graph Convolutional Network, harnessing artificial intelligence for precise multilabel generation in short videos. It begins by implementing an inter-frame absolute difference method with frame intervals to obtain keyframes. Subsequently, a content-aware dynamic graph convolutional network processes these frames, generating multiple labels rooted in content awareness. Ultimately, a set of key information labels is selected according to specific rules. This method significantly enhances label recognition accuracy, surpassing traditional approaches. Experimental results confirm its efficacy in multi-label recognition for short videos, promising advancements for the field.