| Abstract： |
Due to the rapid development of big data and artificial intelligence technology, images have increasingly become important observational data for philosophical and social sciences, yet few literature systematically summarizes the method and logic of applying image analysis in related field. By constructing a typo-logical framework of "information complexity-information extraction method", the research context and direction of image analysis for philosophy and social sciences are explored. This paper categorizes the methods and characteristics of extracting effective information from image data, and based on this framework, we discuss different research paths on image analysis in philosophy and social sciences. In the future, image analysis in philosophy and social sciences will integrate more artificial, semi-automatic and fully automatic information extraction methods, gradually shifting from mining structural features of images to cognitive features. Thus researchers should strengthen the construction of data sets, develop more convenient integrated analysis tools, fully consider data risk issues such as privacy ethics, and further expand the potential path of applying image analysis methods in the field of philosophy and social sciences. |
