Analysis And Research On Urban Public Administration Events Under The Background Of Big Data

In the information age,with the development of big data application,big data analysis and data mining provide solutions for the study of public administration problems,which is of great significance for the construction of public administration informatization and the promotion of urban security and development.In the face of the increasing amount of public administration event data,how to effectively and accurately apply data analysis and data mining technology to explore the potential rules of public administration events has become a problem that researchers need to consider.This paper,taking Wuhan city community management and service information system as the analysis object,using millions of public administration in Wuhan city event data,demographic information,price information,such as multi-source data,based on the text classification,association mining,spatial analysis and other research methods,tube to Wuhan club events of the relationship between the various data sources of the researches.In this paper,the main research work is as follows: first of all,for social events database grid member and the narrative report of the characteristics of the unstructured,the TF-IDF algorithm to extract the keywords,and by using the C4.5 decision tree classification system to text classification,text data obtained in Wuhan harm the basic information of the public administration and safeguard social events and quantities;At the same time,in order to evaluate the free text classification results of the C4.5 decision tree classifier in the system,this paper compared the classification accuracy of the C4.5 decision tree and the Bayesian classification method,and proved that the performance of the C4.5 decision tree in this experiment was better than that of the Bayesian classification method.Secondly,in order to explore the public administration service events with the corresponding regional social and economic factors and whether there is a certain relationship between geographical environment,in this paper,based on relative fuzzy rule mining technology to explore the level of public administration events and the correlation of various factors,find poverty,education,population density,marital status,community correction workers such as demographic data with negative energy is directly related to public administration events happened.Finally,this paper analyzes the spatial autocorrelation of public administration events in Wuhan through the statistical analysis of Moran’I index,and explores the aggregation and distribution of public administration events among street units,so as to strengthen the real-time supervision of public administration personnel on specific hot spots,and provide an effective basis for resource allocation and strategy formulation of management personnel.In combination with the correlation between public administration events and various factors,this paper uses TOPSIS to comprehensively evaluate the public administration level of each street,providing a new solution for strengthening the maintenance of public administration.In view of the massive increase of public administration events,this paper studies the correlation of public administration event data through data mining and big data analysis,conducts correlation mining analysis on the correlation relationship between social attributes and public administration events,and explores its causes.Based on the implied relationship obtained from the in-depth analysis of public administration data,the comprehensive evaluation of the level of street public administration is conducted,which provides a scientific data basis for urban management and control.