The comprehensive evaluation of urban business under deep learning and Siamese neural network

The intensifying global competition among cities necessitates accurate and efficient evaluations of urban business environments to drive economic growth, attract investment, and foster innovation. Traditional assessment methods, often reliant on expert opinions and manual analysis, are prone to subjectivity and inefficiency. To address these limitations, this study introduces an optimized Siamese Neural Network model designed to improve the accuracy and efficiency of urban business environment evaluations. The model leverages feature extraction and multidimensional learning to analyze key indicators, including economic development, infrastructure integrity, policy friendliness, and market entry difficulty, utilizing publicly available datasets. Additionally, the model incorporates emerging technologies, including the Internet of Behaviors and generative artificial intelligence (AI), to bolster capabilities in capturing and analyzing complex behavioral data. The Internet of Behaviors enables the collection of real-time dynamic behavioral data from various urban activities, providing a comprehensive and detailed understanding of the business environment. Generative AI, on the other hand, generates predictive models from existing data, simulating future trends and scenarios, thereby enhancing the accuracy and foresight of decision-making. Performance comparison experiments demonstrate the model's superiority over baseline models across all evaluation metrics. Specifically, the optimized model achieves F1 Scores of 0.874, 0.879, and 0.882 on the Doing Business Indicators, Urban Land Cover Classification, and Open Cities Artificial Intelligence Challenge datasets, respectively, significantly outperforming the Graph Neural Network for Business Environment and Transformer-based Business Environment Evaluation models. Furthermore, the model exhibits exceptional efficiency, with training times of 29.648 s, 31.327 s, and 32.843 s on the respective datasets. In terms of scalability and adaptability, the model achieves Scalability Scores and Generalization Capabilities of 0.821 and 0.876 on the DBI dataset, demonstrating its effectiveness in handling large-scale, multidimensional data. A comprehensive evaluation of urban business environments revealed specific strengths and weaknesses in cities A, B, and C. City A excelled in economic development (8.5) and infrastructure integrity (9.0) but scored lower in market entry difficulty (5.5). City B showed balanced performance across all metrics, while City C demonstrated strengths in policy friendliness (8.5) and market entry difficulty (8.0) but lower scores in infrastructure integrity (6.5). These results highlight the model's utility in identifying areas for improvement and fostering targeted interventions. This study advances the theoretical and practical application of deep learning techniques in urban business environment evaluation, offering city administrators an efficient and objective decision-support tool. By enabling data-driven policy formulation and resource optimization, the proposed model provides a robust strategy for enhancing urban competitiveness.