Application Effect Evaluation of Intelligent Community Service Platform in Grassroots Governance

Abstract

The purpose of this study is to design an intelligent community service platform applied in grassroots governance. First of all, this paper uses a distributed framework to build a service platform to provide data services for the intelligent community. Secondly, intelligent machine learning models are used to accelerate network training for community data classification tasks. At the same time, the high-precision association mechanism based on multi-source heterogeneous big data is introduced to store different types of topic data into the corresponding topic database, so as to realize the fusion processing of structured, semi-structured and unstructured data. Four kinds of account information are set up, including platform administrators, community administrators, community service units and community residents, to meet the diverse needs of community residents. The simulation test shows that the intelligent community service platform passes the function test, and the maximum change of response time is less than 1%, which proves the feasibility of applying the intelligent community service platform in grass-roots governance.