Big Data Intelligence Empowered Specialized Disciplines Development Pattern Recognition in Power Industry Universities

Universities are concentrating on building industry characteristics to strengthen and expand their influence in the information age. Big data on intellectual output is a key representation of discipline construction. We created an algorithm to identify development features of disciplines, including temporal trend, research hotspots, and mutation characteristics. Using CNKI as data source, with the aid of scientific knowledge graph and social network analysis, 10786 core journal thesis published between 2012 and 2021 demonstrated the co-occurrence, keyword development trajectory, and mutation word development path. According to data mining, high-level intellectual output that is relevant to industry increased in quantity and proportion, and intimacy also improved. High levels of interdisciplinary interaction, a variety of disciplinary innovations, and in-depth disciplinary culture formation should characterize the pattern of disciplinary development. This study is an attempt of specialized disciplines development pattern recognition by big data intelligence, and the recognition algorithms can be used for feature recognition in multidisciplinary fields.