###### Abstract
Therefore, the key to technological development and policy effectiveness depends on the accurate identification of IIC. On the one hand, the accurate identification of IIC can provide systematic suggestions for effective and efficient collaboration and coordination among different regions within the same industry by avoiding intra-cluster competition. On the one hand, effective policymaking for regional industrial development from both spatial and technological perspectives relies on the accurate identification of IIC. Government policies on clusters aim to create an ecological environment conducive to improving the efficiency and reducing the cost of collaboration for firms within clusters. However, the formulation of such policies faces great risks, because how clusters are classified affects the policy subsidies of a large number of enterprises. Traditional cluster identification methods often rely on the administrative region, ignoring the industrial cooperation between different regions and the technical relevance of the cluster. In this paper, the cluster identification method based on patent big data can provide an objective and reasonable tool for the designation of cluster policy. However, the task of precisely identifying IIC has proven to be challenging for policymakers worldwide, including those in China, the US, and EU countries, as innovative industries and enterprises are embedded in every sector of the national economy, both technologically and geographically [3]. In practice, the identification of IIC needs to meet Objectivity-Accuracy-Width-Universality (OAWU) indicators: objectivity (i.e., to avoid the influence of subjective factor), accuracy (i.e., to avoid the omission of related areas or the inclusion of irrelevant areas in the cluster), width (i.e., to ensure the data comprehensiveness), and universality (i.e., to ensure the method can be applied to multiple fields) [15]. Currently, both survey methods and data-based methods are widely used among scholars to satisfy the OAWU criteria. Although common survey methods include questionnaire surveys and personal interviews, which have strong versatility and professionalism, they have some challenges [16; 17]. On the one hand, the data collected from questionnaire surveys are likely limited due to cost and time restrictions. On the other hand, expert interviews have strong professionalism, simple operation, and high universality, but they depend on interviewees' expertise and can be significantly influenced by personal subjectivity. By contrast, data-based methods using data-mining technology based on huge market information have become the mainstream, and they integrate traditional statistical analysis tools with machine learning methods. By obtaining a large amount of related data, data-mining technology identify and cluster spatially distributed objects, including enterprises, scientific research institutions, and even individuals [18; 19]. Hence, with this method, researchers can provide a more comprehensive understanding of the actual situation of IIC with minimized influences from investigators. Previous research on IIC identification through big data mainly focuses on geographical information and largely ignores the influence of specific technological factors involved in the industry, which leads to inaccurate identification results on IIC. For instance, rather than considering the overall technological performance of the cluster from a system perspective, the Moran index model, which is commonly used, can only identify clusters formed by several neighboring units with indicators above the average [20]. In addition, the data used in existing research often refer to the open resources of social and economic data, which can satisfy the requirements of high volume, velocity, variety, veracity, and value (SV). Still, they are less relevant to the technology involved in the industry, which is not likely to meet the identification expectations of IIC.