Smart City Infrastructure Network Risk Assessment and Environmental Resilience Design Improvement Strategy Based on Data Analysis Algorithm

This paper proposes a risk assessment and environmental resilience optimization framework based on multi-dimensional data analysis algorithms to address the lack of systematic risk assessment in the current smart city infrastructure network due to insufficient fusion of multi-source heterogeneous data, weak dynamic risk prediction capabilities, and unclear couple mechanisms of environmental disturbance factors. The study constructs a PB-level urban operation database that integrates traffic flow, energy consumption, communication load, and meteorological disasters, uses an improved random forest algorithm to identify key nodes, and combines the LSTM time series prediction model to simulate the dynamic evolution of risks. By establishing an evaluation system that includes 18 indicators such as network topology strength, functional redundancy, and disaster recovery rate (increased by 40%), the complex network anti-destruction theory is innovative introduced to design a resilience enhancement strategy based on node between optimization. Empirical analysis shows that compared with traditional SVM, the model shortens the fault recovery time of urban water supply networks from 73 min–44 min, improves the stress resistance of key nodes in power networks by 35.6%, and increases the service maintenance rate of communication networks in extreme weather from 53% to 89%. This verifies the effectiveness of the proposed method in improving the environmental adaptability and risk resistance of urban infrastructure, and provides a quantifiable decision support tool for the collaborative optimization of multiple systems in smart cities.