Logic detection method in network culture communication based on semantic relevance

Abstract

With the rapid development of the Internet, it has become an indispensable part of human daily life. Network culture is one of the forms of network information expression, and humans can obtain various information from it. The formation of multiculturalism is inseparable from the effective dissemination of online culture. The information of these cultures not only has an impact on the individual’s life but also brings many changes to the society. It is very valuable to be able to quickly detect and disseminate positive factors in the network culture. In view of this situation, this paper first introduces the classification technology of culture and the introduction of semantic association in network text. With the help of semantic association information in network text, this paper proposes a fusion video of convolutional neural network algorithm for football scenes. Cultural factors in the event are tested. The training text is used for initialization learning, and the training module is used to effectively extract the network text features. Based on the convolutional neural network, a detection model of wonderful events was established, which achieved the correct detection of goals, corner kicks, penalty kicks, and red and yellow cards. It can quickly provide useful cultural information to human beings, enabling such cultures to spread rapidly. The experimental results demonstrate the effectiveness of the proposed method.