Measurement and evaluation of ecological niche in open innovation ecosystem based on large models

Abstract

This paper objectively and comprehensively constructs an evaluation index system for the ecological niche of open innovation ecosystem by integrating existing research. We utilize the long short-term memory network in large model methods and based on the idea of suitability model to conduct in-depth analysis of China's publicly available statistical data from 2000 to 2023. A comprehensive calculation and in-depth analysis of the ecological niche of open innovation ecosystem and basic ecological niche in 30 provincial-level administrative regions (excluding Hong Kong, Macao, Taiwan and Tibet) are carried out. The research results not only reveal the significant differences in the suitability of innovation ecological niche among different provincial-level administrative regions, but also show the complex relationship between ecological niche suitability and evolutionary momentum, as well as the different characteristics and development trends of each basic ecological niche. Compared to traditional measurement methods, the large model approach adopted in this study demonstrates significant advantages: (1) It is capable of processing massive and complex datasets, capturing the dynamic changes within innovation ecosystems; (2) By utilizing Long Short-Term Memory (LSTM) networks, it effectively addresses the vanishing gradient problem inherent in traditional RNN models, thereby enhancing prediction accuracy; (3) In conjunction with fitness models, it provides a more comprehensive assessment of the internal mechanisms and external environmental factors of innovation ecosystems. This research provides important theoretical basis and empirical support for in-depth understanding of the innovation ecosystem in various provincial-level administrative regions of China, and helps to better grasp the pattern and direction of national innovation development and provide a powerful reference for formulating scientific and reasonable innovation policies.