Research on the analysis and application of technological supply and demand structure based on LDA and BERTopic models

This paper employs text mining techniques, specifically Latent Dirichlet Allocation (LDA) and BERTopic topic models, to conduct an in-depth investigation of the supply and demand structure of regional scientific and technological achievements. The objective is to identify imbalances in supply and demand, thereby providing novel insights for enhancing the efficiency of technology transfer. The research findings indicate that the LDA model outperforms the BERTopic model in this study. Taking Guizhou Province, China, as a case study, the LDA model analysis categorizes the demand side into 16 domains and the supply side into 18 domains, both exhibiting a "long-tail distribution" characteristic. Further analysis reveals a structural imbalance in the supply and demand of scientific and technological achievements in Guizhou Province. For instance, there is a high demand in areas such as mineral extraction and utilization, as well as digital and intelligent applications, accounting for 20.3% and 14.3% respectively, yet the supply is insufficient, with only 5.1% and 3.1% respectively. Conversely, areas like mechanical processing, and bridge and building construction experience an oversupply, with the supply accounting for 17.9% and 13.8% respectively. Addressing the structural imbalance in the supply and demand of scientific and technological achievements, this study proposes development recommendations from three perspectives: policy and management systems, regional collaboration, and ecological construction. The aim is to optimize the supply and demand structure of scientific and technological achievements in Guizhou Province and promote the deep integration of technology and the economy.