###### Abstract  
Innovative Industrial Clusters (IIC), characterized by geographical aggregation and technological collaboration among technology enterprises and institutions, serve as pivotal drivers of regional economic competitiveness and technological advancements. Prior research on cluster identification, crucial for IIC analysis, has predominantly emphasized geographical dimensions while overlooking technological proximity. Addressing these limitations, this study introduces a comprehensive framework incorporating multiple indices and methods for accurately identifying IIC using patent data. To unearth latent technological insights within patent documents, Latent Dirichlet Allocation (LDA) is employed to generate topics from a collection of terms. Utilizing the applicants' names and addresses recorded in patents, an Application Programming Interface (API) map systems facilitates the extraction of geographic locations. Subsequently, a Multivariate Density-Based Spatial Clustering of Applications with Noise (MDBSCAN) algorithm, which accounts for both technological and spatial distances, is deployed to delineate IIC. Moreover, a bipartite network model based on patent geographic information collected from the patent is constructed to analyze the technological distribution on the geography and development mode of IIC. The utilization of the model and methodologies is demonstrated through a case study on the China flexible electronics industry (FEI). The findings reveal that the clusters identified via this novel approach are significantly correlated with both technological innovation and geographical factors. Moreover, the MDBSCAN algorithm demonstrates notable superiority over other algorithms in terms of computational precision and efficiency, as evidenced by the case analysis. b  
B 2024 2  
within clusters clearly outperform enterprises outside the clusters in terms of intensity, cooperation, and the number of achievements in Research and Development (R&D) [6]. In recent years, with the rapid development of new technologies such as integrated circuits, artificial intelligence, and biological sciences, technology and those resources are simultaneously concentrated, forming a new form of industrial cluster, which is defined as IIC. Unlike traditional industrial clusters, which depend on natural resources and human resources, IIC have created significantly higher profits since they increase the innovative ability and the application of intelligent resources of the enterprise. The most well-known example is Silicon Valley in California and the chemical engineering industry clusters in Boston [7; 8]. The recent success of technological development in China followed a similar path, such as Beijing Zhongguancun Mobile, Zhejiang Shaoxing Textile Machinery, and Fujian Jinjiang Sports [9; 10]. Therefore, they have been implementing supportive policies to continuously encourage the development of IIC. For instance, the \"Guiding Opinions on Further Promoting the Development of Industrial Clusters Strategy in 2015\" and the \"Interim Measures for Promoting the Development of Characteristic Industrial Clusters of Small and Medium-sized Enterprises (SMEs) Strategy in 2022\" aim to identify around 200 local IIC across China and assist them in becoming larger IIC from the province-level [11; 12; 13]. The competition for technological resources between countries has shifted from single-technology competition to competition for IIC. Identifying technology clusters from the perspective of patented technologies helps enterprises and R&D personnel to understand the technology structure and explore technology opportunities so as to gain advantages in technology R&D and competition. It is of great significance to promote the specialized division of labor and collaboration among enterprises, effectively allocate production factors, reduce the cost of innovation and entrepreneurship, save social resources, and promote regional economic and social development [14].