Abstract: The development of intelligent manufacturing, the mainstay for a manufacturing power, has a close relation with China’s global status in future manufacturing industry, and plays an important role in expediting modern industrial system, consolidating and sharpening the foundation of real economic development, fostering the new development form as well as building Digital China. Under the current circumstance, China’s intelligent manufacturing industry, still in development stage, has a long way to go to catch up with that of developing countries. What need to pay more attention lies in the following items: how to focus on intelligent manufacturing, advance the integration of digitalization and industrialization, inject incentives to firms’ innovative network development and enhance their innovative capability. With the public companies in the demonstration project of intelligent manufacturing issued by the Ministry of Industry and Information Technology as researching samples, the paper chooses R & D as an indicator of intelligent manufacturing companies in innovative investment, and patents as the indicator in innovative production. This paper uses DEA cross efficiency model to measure innovation efficiency of Chinese intelligent-manufacturing enterprise, uses amendment gravity model to confirm network correlation relation of innovation efficiency of Chinese intelligent-manufacturing enterprise, use community algorithm and secondary assignment program to reveal the evolution law and driving factors of network structure. The innovative efficiency network consists of 4 sectors, with uneven innovative correlations between enterprises and low integration between highly cohesive subgroups within each sector. For instance, Jiangsu, a China’s Eastern province that witnesses a higher regional economic development and intensive intelligent manufacturing companies locally or in nearby provinces, stands on the centre of innovative efficiency network in terms of intelligent manufacturing companies. Thus, it has a strong correlation with local enterprises and that of nearby provinces. Nevertheless, it can hardly boost the innovative efficiency of trans-regional intelligent manufacturing companies, merely shows the spillover effect for intelligent manufacturing companies in nearby provinces. And the intelligent manufacturing companies in Western China like Xinjiang, Shaanxi, has a stronger independent correlation and not in the grip of other intelligent manufacturing companies as their positions are is the edgy of innovative efficiency network. Spatially, the innovative efficiency network community shows a evolution from regional community that prioritises neighbouring regions to transregional community. In the beginning of China’s intelligent manufacturing companies, the innovative efficiency network was driven mainly by geographical proximity. While the rapid development gradually weakens this effect and the industrial proximity and social proximity comes into force. On this ground, the innovative efficiency network will evolve to regional obscurity in a “Core - Edge” form for community structure. Based on our researching conclusion and the realities in China’s intelligent manufacturing industry, three policies need to take into considerations: First is to pay attention to the spillover effect of innovative efficiency network space in intelligent manufacturing companies, with key enterprises playing leading role in innovation. Second is to balance development levels in different regional intelligent manufacturing enterprises. Third is to give a full push to the innovative development in main intelligent manufacturing sectors. From the vision of community structure, the paper analyses the drivers to evolve efficiency network in intelligent manufacturing companies and investigates the influences that the multi-dimensional proximities of intelligent manufacturing companies in different development stages shows to network evolutions, which provides an reference to when and where to build innovation network for intelligent companies, and fills up the theoretical gap for the research in evolutionary and driven ingredients of innovative efficiency network in intelligent manufacturing companies.