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In recent years, the convergence of the Internet of Things (IoT) and machine learning technologies has instigated advancements in IoT, and machine learning has revolutionized SMEs, reshaping their role in regional economic growth. These technologies provide SMEs with innovative tools, fostering connectivity within regional ecosystems. This transformation is crucial for China's economic realignment in its 'new normal' phase, with enterprises driving social and economic progress. This study proposed the XGBoost algorithm and IoT data enhance collaboration among SMEs in regional economies, fostering innovation and driving economic development. The role of IoT and machine learning in promoting sustainable regional economic prosperity. Technological innovation is essential for enterprise survival and growth in a competitive landscape. This paper examines the influence of environmental factors and geography on economic development across China's regions. It focuses on SME coupling and coordination, contributing to regional economic progress through performance evaluation. IoT integration provides SMEs with abundant real-time data, enabling deep insights into customer behavior, supply chains, and production. Simultaneously, the XGBoost algorithm efficiently processes and extracts actionable insights from this data. Data from 11 provinces in the Yangtze River economic belt highlights Jiangsu Province's leadership in regional innovation performance for 2015 and 2020. The empirical results, grounded in datasets amalgamating information from these provinces, attest to the potential of this IoT and XGBoost-driven approach. Boasting an impressive accuracy rate of 91.7%, surpassing alternative machine learning techniques like SVM, KNN, DT, RF, and LR, this research underscores the effectiveness of this integrated strategy in optimizing SME operations. Additionally, it computes the coupling coordination degree, innovation environment ranking, mean value, and numerical changes from 2010 to 2020, with an average innovation coupling coordination degree of 0.1624 across 30 Chinese provinces. [ABSTRACT FROM AUTHOR]

To accurately evaluate the development level of ecological civilization in coastal cities, this paper proposes an evaluation algorithm of coastal city ecological civilization development based on Improved BP neural network, constructs the evaluation index system, and establishes the BP neural network-based evaluation model. By collecting the relevant data of 30 cities in China Line standardization, as the input learning matrix, entropy and coefficient of variation are used to evaluate the development level of ecological civilization. Through 5000 training times, the error accuracy is close to 10–7, and the cities with low ecological civilization level and less cultivated land resources per capita are found out. Based on the improved BP neural network method, according to the development level of coastal cities, the ecological culture of coastal cities is put forward as a whole. The countermeasures and suggestions of construction are given. • In this study, the ecological civilization development level of the coastal cities in Jiangsu was evaluated. • The evaluation results showed the overall development level of ecological civilization. • In the assessment, the comprehensive level of the secondary indexes is better than that of Donghai and Guanyun. • Jiangsu coastal cities have higher ecological civilization development levels, and Donghai and Guanyun is relatively low. [ABSTRACT FROM AUTHOR]