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.

中文翻译：

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基于语义相关性的网络文化交流中的逻辑检测方法

随着互联网的飞速发展，它已经成为人类日常生活中不可或缺的一部分。网络文化是网络信息表达的一种形式，人们可以从中获得各种信息。多元文化主义的形成与网络文化的有效传播密不可分。这些文化的信息不仅影响个人的生活，而且给社会带来许多变化。能够快速发现和传播网络文化中的积极因素非常有价值。针对这种情况，本文首先介绍了文化的分类技术以及网络文本中语义关联的介绍。借助网络文本中的语义关联信息，本文针对足球场景提出了卷积神经网络算法的融合视频。测试中的文化因素。训练文本用于初始化学习，训练模块用于有效提取网络文本特征。基于卷积神经网络，建立了奇妙事件的检测模型，可以正确检测到目标，角球，罚球以及红黄牌。它可以迅速为人类提供有用的文化信息，从而使这种文化迅速传播。实验结果证明了该方法的有效性。训练模块用于有效提取网络文本特征。基于卷积神经网络，建立了奇妙事件的检测模型，可以正确检测到目标，角球，罚球以及红黄牌。它可以迅速为人类提供有用的文化信息，从而使这种文化迅速传播。实验结果证明了该方法的有效性。训练模块用于有效提取网络文本特征。基于卷积神经网络，建立了奇妙事件的检测模型，可以正确检测到目标，角球，罚球以及红黄牌。它可以迅速为人类提供有用的文化信息，从而使这种文化迅速传播。实验结果证明了该方法的有效性。