The interpretation of entropy and negative entropy theories on the nature of information is the first scientific paradigm on the interpretation of the nature of information at the beginning of the rise of contemporary information science. The information entropy or negative entropy theories are the relative measurement for the structuralization degree of the system at a specific level, which have certain characteristics of relativity, functionality, and quantification. Although the concepts of entropy, negative entropy, information, entropy increase, and entropy decrease often have very different specific meanings in different disciplines and for different scientists, the meanings of these concepts are unified in essence and the differences between them are generated when the same kind of concepts is applied to the study of different directions of the same kind of phenomena: either to the static structuralization degree of the system or to the change of the dynamic structuralization degree of the system. Since entropy and negative entropy theories only measure the relationship of structural differences at a specific level of the system from a formal aspect, they are not aimed at the information itself but rather they are aimed at only the structuralization characteristics of the information carrier itself at a specific level. Because of this, it is impossible to deduce the general nature of information directly from such theories.

The achievement of artificial intelligence has been one of the goals in the field of machine learning, and achievements in the field of deep learning have led to the idea that the goal of so-called “intelligence” in artificial intelligence can be achieved with deep learning. From the perspective of philosophy of information, intelligence arises from the information activity of the “subject”. In this paper, we compare the current approaches of deep neural networks and deep learning with the information activity system of the “subject” proposed by philosophy of information, and point out the limitations of deep learning to achieve artificial “intelligence”.

The theory of information ontology proposed by the Chinese philosophy of information not only changes the connotation and limit of “existence”, but also lays a foundation for the discussion of the originally illusory “non-existence” world from the level of philosophical abstraction. However, the existing information ontology still has some aspects that need to be expanded and deepened, and there are several problems that need to be solved. The new information evolution ontology can provide some constructive answers to solve these problems.

Lost Book Compilation (Ji Yi Theory) depends on information collation and summary to recover lost books and culture. There have been some holographic phenomena in the process of natural evolution, but such holographic phenomena are also reflected in the process of cultural evolution in human society. The phenomenon of holographic insufficiency exists in both nature and social culture. The holographic phenomenon of social culture is reflected in the corresponding characterization and mapping between books and books, society, era. The holographic nature of social culture is reflected in both horizontal and vertical aspects, that is, contemporary thought and the inheritance and development of historical thought.

When we discuss education under the background of the development of artificial intelligence, we must first analyze what algorithm-based artificial intelligence can and cannot do. Our education should focus on training people to learn and exercise the abilities that artificial intelligence is not good at or lacks. Only in this way can we give full play to the greatest potential of human intelligence under the blessing of artificial intelligence. Philosophy of information puts forward the idea of an information creation system to describe what kind of information creation ability real intelligence should have. Considering the functions of 9 subsystems, AI has completely or simply possessed some of the subsystem functions, but it still seriously lacks the capabilities of three subsystems. The future education of information creation is launched for the lack of abilities of these three artificial intelligences.

For improving and developing self‐organization theory in contemporary complex information systems theory, it is necessary to reinterpret related concepts and their relations and construct a conceptual system related to self‐organization theory. Based on the latest research results, this article specifically discusses concepts of organization and disorganization, static organization and dynamic organization, static disorganization and dynamic disorganization, organizing and disorganizing, self‐organization and other‐organization, self‐disorganization and other‐disorganization, and their relations. Further, it explores the mechanism of organization and disorganization, the compatibility of organization and disorganization, feedback and mutual other‐disorganization in interaction, constraint and freedom of organization and disorganization, complexity of restraint and external factors, and relations between “the good” and “the evil” in restraint and freedom. On this basis, a diagram of the conceptual system of organization and disorganization, including four levels and their relations, is presented.