Existing methods for monitoring internet public opinion rely primarily on regular crawling of textual information on web pages but cannot quickly and accurately acquire and identify textual information in images and videos and discriminate sentiment. The problems make this a challenging research point for multimodal information detection in an internet public opinion scenario. In this paper, we look at how to dynamically monitor the internet opinion information (mostly images and videos) that different websites post. Based on the most recent advancements in text recognition, this paper proposes a new method of visual multimodal text recognition and sentiment analysis (MTR-SAM) for internet public opinion analysis scenarios. In the detection module, a LK-PAN network with large sensory fields is proposed to enhance the CML distillation strategy, and an RSE-FPN with a residual attention mechanism is used to improve feature map representation. Second, it proposes that the original CTC decoder be replaced with a GTC method to solve earlier problems with text detection at arbitrary rotation angles. Additionally, the performance of scene text detection for arbitrary rotation angles is improved using a sinusoidal loss function for rotation recognition. Finally, the improved sentiment analysis model is used to predict the sentiment polarity of the text recognition results. The experimental results show that the new method proposed in this paper improves recognition speed by 31.77%, recognition accuracy by 10.78% on the video dataset, and the F1 score of the multimodal sentiment analysis model by 4.42% on the self-built internet public opinion dataset (lab dataset). The method proposed provides significant technical support for internet public opinion analysis in multimodal domains.

Presently, road and traffic control construction on most university campuses cannot keep up with the growth of the universities. Campus roads are not very wide, crossings do not have lights, and there are no full-time traffic management personnel. Teachers and students are prone to forming a peak flow of people when going to and from classes. This has led to a constant stream of traffic accidents. It is critical to conduct a comprehensive analysis of this issue by utilizing voluminous data pertaining to school traffic incidents in order to safeguard the lives of faculty and students. In the case of domestic universities, fewer studies have studied knowledge graph construction methods for traffic safety incidents. In event knowledge graph construction, the reasonable release and recycling of computational resources are inefficient, and existing entity–relationship joint extraction methods are unable to deal with ternary overlapping and entity boundary ambiguity problems in relationship extraction. In response to the above problems, this paper proposes a knowledge graph construction method for university on-campus traffic safety events with improved dynamic resource scheduling algorithms and multi-layer semantic graph convolutional neural networks. The experiment’s results show that the proposed dynamic computational resource scheduling method increases GPU and CPU use by 25% and 9%. On the public dataset, the proposed data extraction model’s F1 scores for event triples increase by 1.3% on the NYT dataset and by 0.4% on the WebNLG dataset. This method can help the relevant university personnel in dealing with unexpected traffic incidents and reduce the impact on public opinion.

To support campus security governance, especially campus traffic safety management, many ternary extraction techniques in knowledge graphs rely on character-level text analysis; however, the differences between word semantics and overall word meanings often lead to ambiguity and overlap in ternary extraction. Traditional methods are difficult to effectively manage overlapping relationships in text, which seriously damages the flexibility and extraction accuracy of the dataset. In addition, entity separation significantly affects entity relationship triplet extraction, and weak associations between remote entities often blur entity boundaries and reduce recall rates. This study offers the MGRel entity relationship extraction model, which integrates dependent syntactic analysis with a graph neural network to address the issues above and enhance knowledge acquisition for campus security scenarios. Firstly, by incorporating the dependent syntactic parser alongside the dual analysis mechanism of global semantic dependency and syntactic dependency, it effectively captures long-distance semantic associations and enhances entity relationship recognition accuracy; secondly, it devises the architecture of a hierarchical semantic graph convolutional neural network to facilitate the fine-grained extraction of deep implied semantic features among entities; finally, the attention-driven multi-feature fusion module is presented to improve the discriminative capacity of the ternary classifier via a noise filtering approach. The experimental results on three core general-purpose benchmark datasets—NYT, WebNLG, and DuIE—show that the F1 score of this model increases by 1.3, 0.4 and 3.2%, respectively, compared with the current optimal model, demonstrating a considerable advantage over the comparative techniques and potential value for campus security–oriented campus traffic safety applications.