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In this study, we propose a big data-based method for characterizing the feature distributions of multiple technologies within a specific domain. Traditional approaches, such as <i>Gartner’s hype cycle</i> or <i>S-curve</i> model, portray the developmental trajectory of individual technologies. However, these approaches are insufficient to encapsulate the aggregate characteristic distribution of multiple technologies within a specific domain. Thus, this study proposes an innovative method in terms of four proposed features, namely <i>versatility</i>, <i>significance</i>, <i>commerciality</i>, and <i>disruptiveness</i>, to characterize the technologies within a given domain. The research methodology involves that the features of technologies are quantitively portrayed using the representative keywords and volumes of returned search results from <i>Google</i> and <i>Google Scholar</i> in two-dimensional analytical spaces of <i>technique</i> and <i>application</i>. We demonstrate the applicability of this method using 452 technologies in the domain of intelligent robotics. The results of our assessment indicate that the versatility values are normally distributed, while the values of significance, commerciality, and disruptiveness follow power-law distributions, in which few technologies possess higher feature values. We also show that significant technologies are more likely to be commercialized or cause potential disruption, as such technologies have higher scores in these features. Further, we validly prove the robustness of our approach by comparing historical trends with the literature and characterizing technologies in reduced analytical spaces. Our method can be widely applied in analyzing feature distributions of technologies in different domains, and it can potentially be exploited in decisions like investment, trade, and science policy.