Design and Application of a Media Big Data Processing and Analysis Model Based on Artificial Intelligence

With the advent of the big data era, the amount and diversity of media data have grown exponentially, posing a major challenge to traditional data processing techniques. This paper studies an intelligent model to address these challenges, which uses deep learning and natural language processing to automatically extract and predict target features and achieve high-precision predictions, thereby improving the intelligence of the algorithm. A model that integrates convolutional neural networks and recurrent neural networks is established to effectively analyze multimodal data. It has an average accuracy of 89.072% in classification tasks and 89.184% in regression tasks, which is better than traditional methods such as sequence pattern mining, OLAP, and RDBMS. In addition, the model performs well in clustering tasks. In terms of efficiency, the model takes only 0.53 seconds on average to complete the task, with a CPU usage of 32.7%, a memory usage of 514MB, and an energy consumption of 0.045 units, all of which are better than the comparison methods.