Video Trend Prediction Based On Convolutional Neural Networks

With the rapid development of Internet technology in the world,online video has become one of the important industries of the Internet.Compared with tra-ditional media such as TV,newspapers,and newsletters,online video has a huge number of users,attention and video data.With the development opportunity of Web2.0,the Internet has become more open and extensive.Among them,video sharing websites use individual users as the main source of online video,making the contents of the programs maximised and the number of videos exploding.growth of.At the same time,popular videos can bring huge traffic to websites,attract more users to watch,and bring more benefits to video websites and video uploaders themselves.At present,the prediction of the popular trend of video websites is still in the development and testing stage.The mainstream is based on the text and time series characteristics of the video,and simple prediction is based on different algorithm models.Although it is feasible,the accuracy rate is not too high and cannot be effectively applied to the actual situation.In the coming years,it is an urgent problem to predict the trend of different videos on a video sharing website and quickly locate the next popular video.This article is based on the data on the YouTube video site,defines the video trend forecast as a regression problem,combines the social characteristics of the site,and proposes to add the characteristics of the video uploader account quality,video location,and Google Trends that influence the trend of the three trends.The popular deep learning algorithm CNN convolutional neural network constructs the DLVP prediction model.Firstly,combining the characteristics of the video trend,we crawled and preprocessed the text,time series,account quality,and Google trend features of different videos;then we used K-means clustering algorithm to cluster the datasets and finally clustered the datasets.The train sets are respectively substituted into the DLVP model to solve the parameters in the model,so as to establish the best video fashion trend prediction model.Through experimental tests,the author finds that the addition of account qual-ity,video position,and Google Trends can reduce the error and improve the pre-diction accuracy.The final mean square error can be controlled below 0.0143,and the prediction accuracy rate can reach 73.98%.Google Trends contributes most to the improvement of forecast accuracy.Finally,through experimental comparison.and analysis with some existing prediction methods,it is proved that the DLVP prediction model chosen in this paper can be realized,reliability and high efficiency.