This study proposes an effective method to predict the development of technology from an evolutionary perspective. Product evolution is the result of technological evolution and market selection. A phylogenetic network is the main method to study product evolution. The formation of the dominant design determines the trajectory of technology development. How to predict future dominant design has become a key issue in technology forecasting and new product development. This study uses machine learning methods, combined with product evolutionary theory, and constructs a Fully Connected Phylogenetic Network dataset to effectively predict the future dominant design.

This study presents an algorithm for constructing a product phylogenetic tree that can observe product evolutionary patterns. This study was based on previous studies of modeling technology and science evolution that used natural language processing techniques and network analysis, and a product phylogenetic tree construction algorithm that can clearly reflect the complexity of products and the ancestor-descendant relationship between products was developed. To verify the proposed algorithm, a mobile product phylogenetic tree was empirically constructed using 9,803 mobile product feature data released from 1995 to 2019, and smartphone product speciation, feature phone and pseudo-smartphone extinction, and a Chinese mobile product rise in the late 2010s were observed through the mobile product phylogenetic tree. In addition, it was possible to observe the behaviors of firms that produced products through a product phylogenetic tree: the existence of Apple in the mainstream of mobile products since 2007, the catch-up strategy of Samsung, the extinction of Nokia due to incorrect operating system Symbian adoption, and the strategic failure of LG. The fact that the behaviors of products and firms observed through the mobile product phylogenetic tree explain the actual events well demonstrate that the proposed product phylogenetic tree construction algorithm is appropriate. In conclusion, this study contributes to the improvement of the previous product phylogenetic tree construction methodology by adapting natural language processing and network analysis. It is expected that the product phylogenetic tree construction algorithm in this research can be established as a basic approach for product evolution and product innovation research.