Page 108 - Journal of Library Science in China, Vol.47, 2021

AN Lu, CHEN Miaomiao, SHEN Yan & LI Gang / The basic categories and core propositions of information science with Chinese characteristics 107

(STU for short, S for source-oriented, T for transition-oriented, U for information user-oriented)
marks the beginning of multiple modes of information science methods in China. Although the
methodological systems with Chinese characteristics proposed by scholars are based on different
theoretical perspectives and classification criteria, the coexistence of multiple models reflects the
divergent dialectical thinking and the improvement of research level. Many systems still need to
stand the test of time before they mature. Based on the above analysis, this paper puts forward the
following proposition about Chinese information science methodology:

Proposition No.15: Information science methods with Chinese characteristics show a multidisciplinary
cross-integration and vigorous development trend, forming a situation in which multiple models coexist.

As mentioned above, the birth of big data has shifted intelligence research into a data-intensive
research paradigm. Its deep logic is that big data technology forces intelligence research methods
[51]
and approaches to be updated. This is why data science emerged . The methodological system
of information science with Chinese characteristics is a system of coexistence of multiple models.
This is a product of the age. Only when the methodology could keep pace with the times, can it
broaden the research horizon and make its theoretical research innovative. In the past, traditional
intelligence research methods in the context of interdisciplinary fields were numerous and
complex. At the same time, influenced by the amount, the type, and the value of the data, its
methodology was usually also oriented towards specific structured data/intelligence and prior
knowledge. Today, structured, unstructured, and heterogeneous data is disorganized because
of its skyrocketing. Ordinary data processing and analysis are difficult to truly reflect the laws
of intelligence. Therefore, big data technology can just reshape the external environment of
[52]
information science . For example, the improvement of processing technology could turn a large
number of scientific calculations into logical thinking, and artificial intelligence technology makes
the era of full automation arrive. These new technologies, on the one hand, reduce the difficulty of
information processing, and on the other hand, promote the reflection of intelligence researchers
on the relationship between intelligence problems and its research methods. Through the extraction
and identification of the intelligence method system adapted to the new data environment, the
research method set for different intelligence tasks and intelligence objects is formed, which
guides intelligence workers to complete intelligence research much better. It could be seen that
under the influence of big data technology, information science can only absorb new methods and
new theories, and use knowledge discovery methods (such as machine learning, knowledge graph,
etc.), data mining methods, multi-data fusion methods to innovate, and then formed a knowledge-
centered information science system, so as to realize the transformation of information science
with Chinese characteristics to intelligent decision-making. Methodological systems adapted to
Chinese characteristics and intelligence-specific systems have also changed and innovated. If
simply inheriting the existing methodology of the past, it might still be able to achieve results,