With the rapid development of the manufacturing industry, demand forecasting has been important. In view of this, considering the influence of environmental complexity and diversity, this study aims to find a more accurate method to forecast manufacturing industry demand. On this basis, this paper utilizes a deep learning model for training and makes a comparative study through other models. The results show that: (1) the performance of deep learning is better than other methods; by comparing the results, the reliability of this study is verified. (2) Although the prediction based on the historical data of manufacturing demand alone is successful, the accuracy of the prediction results is significantly lower than when taking into account multiple factors. According to these results, we put forward the development strategy of the manufacturing industry in Guangdong. This will help promote the sustainable development of the manufacturing industry.

This study aims to analyze the development trend of the manufacturing industry transformation and upgrading in the Guangdong–Hong Kong–Macao Greater Bay Area (2008–2018). On the basis of synergetics, the order parameter method of factor analysis is used to study these factors. The results show that: (1) There are five slow variable factors, such as intelligent manufacturing industry, technological innovation, scale agglomeration, market demand, and fixed asset investment, which are important power sources of the transformation and upgrading of the manufacturing industry in Greater Bay Area. The development of these factors is relatively mature, and they cooperate with each other. (2) Similar to a fast variable of manufacturing development ecology, green development is an important coordinating factor in removing bottlenecks. Finally, suggestions for the development of the transformation and upgrading of the manufacturing industry are put forward.

With the rapid development of high quality industries, it is particularly important to study the sustainable competitiveness of manufacturing and its driving factors. The aim of this paper is to build the whole competitiveness index to analyze the recent development trends of manufacturing in G20 participating countries from 2008 to 2018. Meanwhile, based on the diamond theory, this paper adopted a panel regression model to conduct an empirical analysis on various factors that affect the sustainable competitiveness of manufacturing. These results showed the following: (1) Transport services have the most significant effect on manufacturing in developing countries. (2) Intellectual property only has a positive and significant effect on manufacturing in developed countries. (3) Information technology plays a significant role in all countries, but it is more effective in developed countries. Finally, this paper puts forward some suggestions for the sustainable development of manufacturing.

High-tech manufacturing enterprises, as innovative entities, are a key focus of national attention. Currently, such enterprises are facing both internal governance pressure and external institutional pressure. Unlike traditional studies that mostly use regression equations, this article uses the fuzzy-set qualitative comparative analysis method to examine how high-tech manufacturing enterprises can coordinate their internal governance mechanisms and external institutional pressures to achieve optimal innovation. This improves the complex mechanism of the multiple factors jointly explaining corporate innovation, and also helps to elucidate the nonlinear relationship between internal governance factors, external institutional factors, and corporate innovation, effectively enriching research methods and results. However, there has not been any research on the issue of enterprise innovation from the perspective of coordinating the two, which urgently needs to be addressed. This article examines how high-tech manufacturing enterprises can reconcile their internal governance mechanisms with external institutional pressures to achieve optimal innovation. The results showed that (1) a single factor cannot constitute the necessary conditions for innovation in high-tech manufacturing enterprises, but executive and shareholder governance have universality in the innovation in high-tech manufacturing enterprises; (2) in the absence of political advantages, high-tech manufacturing enterprises should focus on the coordinated development of internal governance, making board, executive, and shareholder governance the core conditions for innovative development; (3) with political advantages as the main focus and market attention as a supplement, high-tech manufacturing enterprises promote innovative development by combining executive and shareholder governance. This finding indicates a significant substitution effect between government legitimacy and board governance, and confirms that the importance of obtaining government legitimacy for high-tech manufacturing innovation is higher than market legitimacy. This article enriches the research on enterprise innovation by linking internal corporate governance with external institutional pressure, expands the research on the coordination relationship between institutional pressure and corporate governance, and has enlightening significance in revealing the collaborative path for innovation in high-tech manufacturing enterprises.

With the rapid development of the economy, it is important to reasonably evaluate the development status of the regional manufacturing industry. Given this, this article expands the evaluation indicators of urban advanced manufacturing (UAM) from the perspective of the push–pull-mooring (PPM). Then, it uses a machine learning (ML) method to predict the evaluation results of other cities through a small amount of sample data. The results show that: (1) From the current development status of UAM in Guangdong Province (GD), cities in the Pearl River Delta region occupy a dominant position. However, cities in eastern, western, and mountainous regions have strong development potential and lead cities. Therefore, each region has cities with high levels of development and has a demonstrative role. (2) By comparison, it was found that the overall development level of UAM in GD is not significantly different from that of the Yangtze River Economic Belt. However, due to significant differences in their extreme values, the proportion of cities above the average in the overall population is relatively small. This indirectly proves that GD’s UAM not only has a phased nature, but also has a demonstrative role. (3) The prediction effect of the perceptron model is better than other methods. Although neural network models have better prediction performance than other machine learning models, they should not overly rely on complex network structure prediction data. By comparing the results, the reliability is verified. Finally, according to the life cycle theory, we propose a targeted development path for different UAM.