The traditional spatial optimization location solution is difficult to solve the space optimization location problem under the condition of large data volume. However, GIS has the advantage of analyzing and processing spatial data, which can effectively compensate for this defect. In this paper, we analyze the enterprise site selection and R&D innovation policy based on BP neural network and GIS system. As a tool for the government to guide, encourage, support and adjust innovation activities and application of achievements, science and technology policy can provide new support for the development of innovation by improving the industrial chain and innovating the industrial structure. Moreover, the quantitative analysis of the entropy weight method and the qualitative analysis of the AHP method are combined to analyze a number of influencing factors. Based on this, the overlay of various factors is further analyzed, and the maximum eigenvalues of the target layer and the criterion layer and the weights of each index are calculated using MATLAB tools. Therefore, according to the different characteristics of different periods and different fields, the government should formulate science and technology innovation policies to improve the specificity and applicability of the policies.
Evaluation of Regional Science and Technology Innovation Policy Effect Based on Lasso and BP Neural Network
