Optimization of Urban Renewal Planning Schemes and Community Vitality Enhancement Strategies Based on Deep Learning Algorithms and Smart City Evaluation

In recent years, with the continuous acceleration of urbanization, urban renewal and community vitality enhancement have become key initiatives to solve the contradictions of urban development. This paper firstly proposes an evaluation system for urban renewal planning program based on ERG theory, and then proposes a smart urban renewal evaluation model based on the combination of hierarchical analysis method and RBF neural network. After the model is constructed, the neural network is utilized to train the index factors so as to determine the correctness of the research in this paper. Finally, Yumen City and Otago City are taken as examples for empirical research, and the comprehensive evaluation value of smart urban renewal in Yumen City is obtained as 0.0545, 0.0313, 0.0436, 0.0328, 0.0499, 0.0369, 0.0194, 0.0365, and 0.058, respectively, which means that the level of urban renewal of Yumen City is still in a relatively elementary state, and Yumen City’s The lowest contribution of ecology to urban renewal is only 12.66%. The average value of the comprehensive evaluation of smart city in Otago city is 0.0435, and its RD value is 0.04628, which means that the level of smart city development in Otago city is slightly higher than that in Yumen city, but the level of urban renewal is still in a relatively elementary state. As a result, this paper proposes strategies to enhance community vitality from three dimensions: environmental vitality, social vitality, and cultural vitality.