The Research On Financing Efficiency Of China's Artificial Intelligence Quoted Companies Based On Shannon Entropy Index Optimized Data Envelopment Analysis Model

As a key area of global technology and industrial transformation,artificial intelligence has become a powerful driving force for sustainable economic development.Compared with developed countries such as Europe and the United States,most of China's artificial intelligence enterprises are in the early stages of growth.Therefore,in-depth study of the financing efficiency of the artificial intelligence industry is of great significance to the sustainable and healthy development of the industry.On the whole,from the static and dynamic perspectives,the cross-section data and panel data of AI listed companies were evaluated for financing efficiency.The combination of dynamic and static forms a relatively scientific and sound evaluation method,which is certain for relevant enterprises and government departments.Reference value.Specifically,the static analysis hierarchy,the pure technical efficiency of each decision-making unit in the traditional BCC model is used as the dependent variable,and the external environmental variables(as the independent variables)that affect the financing efficiency of the enterprise are established.The Tobit regression model is established and verified from the statistical point of view.The linear effect of external environmental factors on the financing efficiency of the DEA model.Secondly,in view of the existing literature on the lack of recognition ability and evaluation index in the empirical study of financing efficiency,the Shannon entropy information metric index is introduced,which not only considers the given input and output index system,but also examines all from the perspective of variable combination.The financing efficiency of the input-output indicator system is easier to identify and more stable and effective than the single indicator system.In addition,in response to the undesired output problems encountered in the field of financing efficiency,the innovative eco-efficiency model was applied to the field of artificial intelligence financing,and the expected efficiency(EFE),undesired efficiency(UFE)and low efficiency were calculated.PE)Three groups evaluated the index scores and complete rankings of corporate financing efficiency,and compared the results with the CCR model and the Shannon entropy index model.The accuracy of the low-efficiency model for the evaluation of undesired output efficiency was illustrated.Dynamic analysis level,the decomposition of total factor productivity into technical efficiency change index,technological progress index,pure technology efficiency change index and scale efficiency change index,more detailed display of the internal indicators of the artificial intelligence industry financing efficiency changes,but also clever combination Shannon entropy optimization model and low efficiency model study the trend of financing efficiency of artificial intelligenceindustry from three perspectives: internal decomposition of Manquist index,complete ranking comparison and undesired output impact.The results show that the comprehensive financing efficiency of China's artificial intelligence listed companies is relatively low,mainly reflected in the pure technical efficiency,but the scale efficiency has not changed much.This shows that the industry's overall financing technology is not high,and still has a large input and output.space.The financing efficiency of the environmental variables removed by Tobit regression shows that the real comprehensive efficiency of China's artificial intelligence listed companies has improved,and the management level of funds has reached a higher level,but the scale efficiency is still low,which also indicates the environmental variables.It has a great impact on the efficiency of artificial intelligence industry financing.In addition,from the lateral comparison results of Shannon entropy index optimization model,low efficiency model and traditional CCR model,the financing efficiency of the three is basically consistent,which also verifies the validity and reliability of the results,and the financing efficiency results can be Giving relevant enterprise individuals and the government a certain quantitative reference basis has great practical significance for the healthy and orderly development of the artificial intelligence industry.