Abstract: Accelerating the high-level building of an innovation ecosystem is the path for building an innovative country. In this study, 30 Chinese provinces (excluding Hong Kong, Macao, Taiwan, and Tibet) are used as examples of case studies. To start with, This article expands the "energy level theory" and comprehensively considers the openness, dynamism, and regionality of regional innovation ecosystems, and builds an indicator system from two dimensions："energy level structure" and "energy level effect" to gauge the energy level of local innovation ecosystems. Secondly, this work applies the resource orchestration theory to the study of innovation ecosystems, and distinguishes two new digital production elements (digital platforms, data resources) and four traditional innovation resources and capabilities (technological talents, R&D capital, industrial diversification agglomeration, and industrial specialization agglomeration) of the innovation ecosystem in the digital context. Finally, the fsQCA technique is utilized to investigate the configuration paths that influence the energy level of regional innovation ecosystems. As a result, it makes clear the guidelines for resource orchestration in regional innovation ecosystems within the brand-new framework of empowered "digital intelligence". The findings show that: Firstly, many contemporaneous causal links are shown in the innovation ecosystem's resource orchestration. Causal asymmetry and "various routes" characterize the configuration paths driving regional innovation ecosystem energy level. The concentration of R&D capital performs a pretty universal role in assisting high-capacity innovation ecosystems. Secondly, there are two new paths for enhancing the innovation ecosystem's energy level in the new scenario of "digital intelligence" empowerment: "capital-digital platform- driven " and "talent-capital-digital platform- driven," as well as a typical road: "talent-capital-driven". Thirdly, the primary reason why the regional innovation ecosystem cannot operate at a high energy level is that the region is focused on the creation of industrial agglomerations, lacks the ability to attract traditional innovation resources like talent and capital, and is less sensitive to the application of new production factors like the development of digital platforms and the use of data resources. Therefore, this paper offers the following three recommendations: initially, it's essential to guarantee a sufficient supply of regional R&D funding and emphasize the function that research funding plays as an incentive. Simultaneously, the government should increase oversight of the use of scientific research funds while increasing autonomy in the use of scientific research funding. Moreover, different areas should deepen the application of data innovation, unlock the value of data elements, improve the enabling effect of "data" and "intelligence," and create a regional innovation ecosystem powered by "data intelligence". Last but not least, backward regions should learn from other innovation ecosystems' resource arrangement templates for high-energy operation. For them, it's critical to boost the vitality of innovation resources, rebuild regional innovation capacities and resources, overcome the "lock in effect" of industrial agglomeration, and therefore raise the energy level of the innovation ecosystem. The research contributions of this research are as follows: ① This study extends the application of "energy level theory" to clarify the concept of innovation ecosystem energy levels from two dimensions: "energy level structure" and "energy level effect," enriching the theoretical connotation of "energy level theory" in the field of innovation ecology. ② This paper identifies the driving path configuration that leads to the improvement of regional innovation ecosystem energy levels, analyzes the linkage effect of "traditional + new" resources and capabilities on the improvement of innovation ecosystem energy levels, and expands the application of resource arrangement theory in the field of innovation ecosystem. ③ This research explores the resource allocation patterns of innovation ecosystems in the context of digital intelligence, providing theoretical guidance and reference templates for innovation ecosystems with "insufficient resource utilization" and "not knowing how to allocate resources".