Intelligent Recognition Algorithm for Network Public Opinion Based on Deep Learning

With the development of Internet science and technology, the target requirements of online opinion recognition algorithms are also changing, and intelligent algorithms based on deep learning can effectively cope with the current increasingly complex online opinion recognition analysis. In the whole model construction, how to improve the accuracy rate of the recognition system and reduce the recognition analysis time are the key issues that need to be solved urgently at present. This article analyzes the network public opinion carrier and simulation experiments, applies convolutional neural network algorithm based on deep learning, and combines it with social software's monthly user activity priority ranking. Based on the data results, the following conclusion is drawn: in the context of internet big data, the intelligent recognition system using convolutional neural network algorithm has higher recognition accuracy than traditional recognition systems in various social software systems, and the average growth rate is 10%. At the same time, the recognition time of systems using convolutional neural network algorithms is lower than that of traditional recognition systems, and the average reduction is 2.3 minutes. This indicates that the network public opinion recognition model based on convolutional neural network algorithm has a higher overall system recognition accuracy and efficiency. Finally, the improved model of online opinion identification proposed in this paper is of reference value for facilitating friendly communication on current social platforms.