The data generated through telecommunication networks has grown exponentially in the last few years, and the resulting traffic data is unlikely to be processed and analyzed by manual style, especially detecting unintended traffic consumption from normal patterns remains an important issue. This area is critical because anomalies may lead to a reduction in network efficiency. The origin of these anomalies may be a technical problem in a cell or a fraudulent intrusion in the network. Usually, they need to be identified and fixed as soon as possible. Therefore, in order to identify these anomalies, data-driven systems using machine learning algorithms are developed with the aim from the raw data to identify and alert the occurrence of anomalies. Unsupervised learning methods can spontaneously describe the data structure and derive network patterns, which is effective for identifying unintended anomalous behavior and detecting new types of anomalies in a timely manner. In this paper, we use different unsupervised models to analyze traffic data in wireless networks, focusing on models that analyze traffic data combined with timeline information. The factor analysis method is used to derive the results of factor analysis, obtain the three major public factors and comprehensive factor scores, and combine the results with the BP neural network model to conduct a nonlinear simulation study on local governmental debt risk. A potential semantic analysis model based on Gaussian probability is presented and compared with other methods, and experimental results show that this model can provide a robust, over-the-top anomaly detection in a fully automated, data-driven solution.

With the development of urban economic construction and urban planning, higher requirements are put forward for the government community in the corresponding community management, community service, and other related things. As an important technical means to assist the government and community in management, video recognition technology plays an important role in the accurate management and service of the government and community. Traditional algorithms based on partial differential equations will destroy image edges and image details in video recognition. Based on this, this paper improves the traditional partial differential equation algorithm of image recognition, selects the GAC model based on image segmentation in the main function, and innovatively optimizes the stop function of its equation function, so as to improve the effect of community case image segmentation. In the image smoothing layer, this paper innovatively selects the second derivative based on image processing as the inherent feature of image recognition, so as to solve the rough problem of image edge and improve the processing efficiency of the algorithm. In order to further maintain the details of the relevant images of community cases, this paper integrates the Gaussian curvature driving function on the improved partial differential equation algorithm, so as to protect the details of the smooth region of the relevant recognition video and solve the disadvantages of the traditional algorithm. The experimental results show that the improved partial differential equation algorithm proposed in this paper improves the accuracy of video recognition by about 5% compared with the traditional algorithm. At the same time, the new algorithm can well ensure the detail integrity of the recognized video.