Abstract:    With the vigorous development of digital economy and the deep promotion of industrial digitalization, traditional manufacturing enterprises have accelerated the pace of digital transformation. In this context, advanced equipment manufacturing enterprises urgently need to realize digital transformation in design, production and sales, and build a new model for development. Enterprise digital maturity was defined as the degree of completion in the process of digital transformation. The evaluation and analysis of digital maturity is an important means to promote digital transformation. Therefore, it is necessary to measure the digital maturity of advanced equipment manufacturing enterprises and explore the main restrictive factors. However, there was a lack of research in this field. 
   Firstly, based on the "input-output" perspective, this paper proposed a conceptual model of digital maturity of advanced equipment manufacturing enterprises, including four dimensions: human resource investment, capital investment, efficiency of operation and innovation efficiency. Then, through literature analysis, fuzzy rough set method and stochastic frontier approach, the digital maturity index system with 4 primary indicators and 12 secondary indicators was finally constructed. The "VHSD-EM" evaluation model was used to weight the indicators. First, based on the weights under "VHSD" and "EM" method, two comprehensive scores were calculated respectively. Second, Spearman rank correlation test was used to explore the consistency of the two measures. The results showed that the evaluation results had strong positive correlation, indicating that the two methods had good consistency. The final weight of each index could be obtained by averaging the weights determined by the two methods. 
    Secondly, based on the characteristics of advanced equipment manufacturing industry and the desirability of statistical data, 49 listed companies were selected as the research object. Using data from annual reports of these enterprises, this paper finally measured the digital maturity scores of 49 enterprises from 2015 to 2020. Then, K-means method was used to cluster the evaluation objects. It was observed that there were significant differences in the digital maturity of advanced equipment manufacturing enterprises, with aggregation effect in distribution. Combined with the definition of maturity model, the digital maturity of advanced equipment manufacturing enterprises was divided into five levels (the highest, higher, average, lower, the lowest). The above maturity hierarchy further enrich the research on capability maturity model in theory and provide new insights for the development of digital maturity model. In practice, it is helpful for advanced equipment manufacturing enterprises to evaluate their own digital transformation level and potential, and then scientifically and comprehensively plan the transformation path.
    Thirdly, based on Tobit model, it was concluded that government subsidies and the development of Fintech had positive impacts on the digital maturity of advanced equipment manufacturing enterprises. Among them, the government′s financial support is an important factor to accelerate the digital transformation. The research enriches the relevant literature on the influencing factors of enterprise digital maturity, further verifies the conclusions of relevant scholars, and provides empirical support for the view that government service resources and means of production are also the key inputs of output in the "input-output" theory. 
   Finally, based on the above analysis, this paper put forward some suggestions to promote the digital maturity of advanced equipment manufacturing enterprises. First, enterprises should increase digital investment in human resources and capital, and formulate reasonable investment plans according to the characteristics of industry attributes, scale, technical advantages and so on. At the same time, enterprises should make full use of digital technologies such as internet of things, cloud computing and big data to improve operation efficiency and innovation efficiency. Second, local governments should increase financial subsidies, promote the development of Fintech, improve the digital and intelligent level of public services and social governance to create good external conditions for the digital transformation of advanced equipment manufacturing enterprises.

Key words: advanced equipment manufacturing enterprise, digital maturity, empirical measurement, influencing factor