Based on text analysis, public big data management is studied. The public data management of Mount Wutai tourism network travel notes is discussed. The positive, neutral, and negative effects of the naive Bayesian classification model and decision tree classification model on the tourism sentiment attitude of Mount Wutai are compared. The relationship between tourism resources, tourism facilities, tourism services, tourism environment, and tourism sentiment and attitude of Wutai Mountain is analyzed. The results show that the true positive rate, true negative rate, and F-measure of the Bayesian decision tree classifier to classify positive text are 86.64%, 81.27%, and 84.62%, respectively. The true positive rate for neutral text is 82.05%, the true negative rate is 78.89%, and the F-measure is 77.11%. The true positive rate for negative text is 83.67%, the true negative rate is 98.29%, and the F-measure is 82.83%. The Bayesian decision tree classifier can evaluate positive and negative texts better than neutral texts. The true positive rate of the C4.5 decision tree classifier for positive text is 91.44%, the true negative rate is 86.57%, and the F-measure is 89.45%. The true positive rate for neutral text is 90.17%, the true negative rate is 83.28%, and the F-measure is 84.06%. The true positive rate for negative text is 91.84%, the true negative rate is 99.05%, and the F-measure is 90.91%. The decision tree classifier has a better evaluation effect on positive and negative texts than on neutral texts. The ROC curve of the evaluation effect of the two classifiers shows that the evaluation effect of the two classifiers has a better evaluation effect on positive text than that of the neutral and negative texts, and the evaluation effect of the C4.5 decision tree classifier is better than that of the Bayesian classifier. The promotion degree of tourism resources and facilities in forwarding online travel notes is obviously higher, and there is a high correlation between tourism resources and facilities and forward online travel notes. In negative online travel notes, the promotion degree of tourism service and tourism environment is high, and the correlation between tourism service and tourism environment and negative online travel notes is high. In summary, improving the quality of tourism services and the tourism environment of Mount Wutai scenic spots can better enhance the recognition and satisfaction of tourists with Mount Wutai tourism.

Smart contracts are widely employed in many industries as a result of the high-quality development of science and economic technology, as well as the introduction of blockchain, which can automatically conduct retrieval, verification, and payment tasks. Smart contracts as an emerging topic, particularly the study of smart legal contracts, must remain forward-looking, and the smart contract sector cannot wait for the legal status of smart contracts to be resolved before advancing. The relative lag of the law becomes unavoidable due to the unassembled and unpredictable character of the law and thus its legislation. In this paper, we explore the incorporation of smart contracts into the scope of legal regulation, the construction of a series of systems for smart contracts, and the prognosis of smart contracts in terms of contract logic, arbitration process, and formal verification from the current law. Furthermore, a smart contract payment template based on semantic-aware graph neural networks is proposed to address the traditional smart contract vulnerability detection payment template method’s low detection accuracy and high false alarm rate, as well as the neural network-based method’s insufficient mining of bytecode-level smart contract features. Experiments comparing the method described in this research to comparable methods reveal that the strategy proposed in this study improves all types of indicators significantly.

With the development of aviation industry, a series of problems have appeared in aviation and airspace, among which the most prominent problem is the congestion of aviation and airspace. Airspace congestion has become a major problem in the development of civil aviation in China. Especially in the central and eastern regions of China, airspace congestion is becoming more and more serious. To better solve the problem of airspace congestion, rough set theory and the Fuzzy C-means (FCM) model are first analyzed. By analyzing the temporal and spatial characteristics of traffic congestion in the control sector, a multisector traffic congestion identification model is established based on radar track data. Four multisector congestion characteristics including equivalent traffic volume, proximity, saturation, and traffic density are established. FCM and rough set theory are used to classify and identify sector congestion. Finally, the model based on FCM-rough set theory is compared with other methods based on the data of the regional control sector in northwest China. The experimental results show that the congestion recognition rate of the model is 92.6%, 93.5%, and 94.2%, and the congestion misjudgment rate is 1.5%, 1.2%, and 1.3%, respectively. Hence, the multisector congestion recognition model has a high recognition rate and a low misjudgment rate, and the overall discrimination result is relatively stable. By comparing the proposed method with other methods, it is concluded that the recognition accuracy of the model based on FCM theory is superior to other methods. In summary, the congestion situation of the sector is affected by a variety of macro- and micro-characteristics of the sector, and the congestion identification model is feasible and efficient. Multisector traffic congestion identification has certain application value for airspace planning, air traffic control-assisted decision making, and air traffic flow management. This work can optimize the aviation and airspace management system and provide relevant suggestions for the study of aviation and airspace congestion.

Social work services have grown in popularity in China in recent years, and in the context of the government's support of social governance innovation, social work has emerged as a powerful tool for intervening in communal public concerns. Community public problems are a focused expression of urban community contradictions and disputes. The aging and damage problems of public facilities in old building areas without property management in urban communities are affecting the lives of residents, and how social workers should intervene in the increasing public problems in communities can no longer be ignored. This article takes the behavior of social workers as the research object. By summarizing and analyzing the existing research results at home and abroad, it clarifies the importance of social workers' behavior to solve the public problems in communities. In order to analyze the group behavior of social workers, a hierarchical analysis model for group behavior identification is proposed by combining deep neural networks. The method uses moderation network migration learning to achieve the detection of temporal consistency of multiple human bodies in behavioral groups; the recognition of individual behaviors with unconstrained duration in-group behaviors is completed through the fusion of spatiotemporal feature learning; the stable and effective recognition of g is achieved through the fusion of individual behavior categories in scenes and the contextual information of interaction scenes. It is experimentally verified that the method can detect social workers' group behaviors, promote the rational solution to community public problems, and drive the development of community multibody governance.