With the rapid development of information technology, the era of Big Data has come. Big Data technology has brought great opportunities for the research of technology mining, while the "data dizzy" and "data redundancy" effects brought by it cannot be ignored. As one of the basic methods of technology mining, the research of scien-tometrics also faces the same opportunities and challenges. In order to meet the challenges, an in-depth analysis of scientometrics was conducted. By using the papers of Scientometrics in SpringLinker Database from 1978 to 2017, a Full-Text citation analysis based on semantic technology is used to quantitatively assess the basic status, landscapes, hotspots and future development trends of the “Scientometrics” research area. Besides traditional methods such as co-word analysis, main path analysis and sleeping beauty paper recognization, novel methods such as dynamic topic model and word vectors models are used, furthermore a three-dimensional visualization technology was proposed. It shows that these methods can provide a dynamic view of the evolution of scientometrics research landscapes, hotspots and trends from various perspectives which may serve as a potential guide for future research.