Intelligent Processing Technology of Cross Media Intelligence Based on Deep Cognitive Neural Network and Big Data

Abstract

This paper studies the cross-media intelligent processing technology based on deep neural cognitive network and big data technology. This paper uses data processing and DNN training algorithms to explore deep cognitive neural networks to study cross-intelligence intelligent processing. The algorithm is mainly based on the rating of the item and the type of the item, and based on the similarity of the item type to give recommendations. This paper conducts data processing by investigating users' item preference and user item ratings. The DNN training algorithm used in this article needs to build a model and determine the method. The model specifically includes an input layer, two hidden layers and an output layer. The data in the input layer of the model is user project preferences, the output layer of the model is user project ratings, the hidden layer adopts the Dropout strategy, and the activation function is ReLU. First, establish the correspondence between item types and user item scores for DNN training, then build a multi-class deep neural network model with a dropout strategy, and finally get the predicted score of the target item after model training and reverse coding. Compared with the collaborative filtering of neural network, the algorithm adopted in this paper does not need to rely on the scores of neighboring users, and only needs to generate recommendations based on the user's personal scores and item types, which is conducive to accelerating the speed of intelligence processing.