A Study on Optimizing the Integration of Police Work into Community Security Governance in Inner Mongolia with Machine Learning Algorithms

Inner Mongolia police have adopted new models like "police grids + community grids" and "community police + grid members + N" to integrate into community governance, achieving certain results. However, issues such as insufficient community police force allocation, inadequate resources for community police work, and a shortage of participating entities in multi - center governance still exist. This study constructs a "data - algorithm - application" closed - loop system using machine learning algorithms, specifically the K-Means clustering algorithm. By preprocessing data, training and verifying the model, and analyzing various relationships like those between community security satisfaction and participation activities, and community policing input and performance, the research aims to optimize the integration of police and civilians in community security governance. The system was piloted in communities in Inner Mongolia to verify the model's effectiveness, shorten the response time to security incidents, and form a replicable "Inner Mongolia smart policing" framework for border areas across the country.