Classification and interaction of new media instant music video based on deep learning under the background of artificial intelligence

With the continuous upgrading and improvement in the Internet and terminal equipment, many instant music videos share information with users through social platforms. This study explores the impact of new media technology on the content of instant music videos on the Internet under Artificial Intelligence (AI) technology to effectively distinguish the elegant and vulgar short videos and improve the quality of short videos on the Internet. Obscene and harmful instant music videos in the massive data are the bottleneck for its development. An improved deep learning model is proposed based on OPEN_NSFW using the AI image detection system technology of the Internet of Things with a powerful processing ability to image information. Experiments demonstrate that this model significantly reduces the false positive rate and improves the recall compared with the traditional machine learning computing model. Besides, it improves the accuracy when discriminating whether the publisher's head image involves eroticism. In addition, this model can identify and classify the main content of instant music videos to optimize the content. This work provides the characteristic basis for the algorithm to judge and protect the original content. Combining algorithm recommendations and strengthening manual intervention promotes online instant music videos' sustainable and healthy development. These findings can provide an excellent technical guarantee and experimental references for the standardized development of the instant music video industry in the future.