Constraints《约束》（季刊）. Constraints provides a common forum for the many disciplines interested in constraint programming and constraint satisfaction and optimization, and the many application domains in which constraint technology is employed. It covers all aspects of computing with constraints: theory and practice, algorithms and systems, reasoning and programming, logics and languages.

Semantic Web《语义网》（双月刊）. The journal Semantic Web – Interoperability, Usability, Applicability is an international and interdisciplinary journal bringing together researchers from various fields which share the vision and need for more effective and meaningful ways to share information across agents and services on the future Internet and elsewhere.

Soft Computing《软计算》（半月刊）. Soft Computing is dedicated to system solutions based on soft computing techniques. It provides rapid dissemination of important results in soft computing technologies, a fusion of research in evolutionary algorithms and genetic programming, neural science and neural net systems, fuzzy set theory and fuzzy systems, and chaos theory and chaotic systems.

AI Magazine《人工智能杂志》（季刊）. AI Magazine (ISSN 0738-4602) is published quarterly in March, June, September, and December by the Association for the Advancement of Artificial Intelligence (AAAI).

Expert Systems《专家系统》（月刊）. Expert Systems: The Journal of Knowledge Engineering publishes papers dealing with all aspects of knowledge engineering, including individual methods and techniques in knowledge acquisition and representation, and their application in the construction of systems – including expert systems – based thereon. Detailed scientific evaluation is an essential part of any paper.

Neurocomputing《神经计算学》（一年48期）. Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.

Neural Networks《神经网络》（月刊）. Neural Networks provides a forum for developing and nurturing an international community of scholars and practitioners who are interested in all aspects of neural networks, including deep learning and related approaches to artificial intelligence and machine learning. Neural Networks welcomes submissions that contribute to the full range of neural networks research, from cognitive modeling and computational neuroscience, through deep neural networks and mathematical analyses, to engineering and technological applications of systems that significantly use neural network concepts and learning techniques.

Artificial Life《人工生命》（季刊）. Artificial Life, launched in the fall of 1993, has become the unifying forum for the exchange of scientific information on the study of artificial systems that exhibit the behavioral characteristics of natural living systems, through the synthesis or simulation using computational (software), robotic (hardware), and/or physicochemical (wetware) means. Each issue features cutting-edge research on artificial life that advances the state-of-the-art of our knowledge about various aspects of living systems. The journal shapes and chronicles the development of artificial life, extending the horizons of biological research beyond life-as-we-know-it and into the domain of life-as-it-could-be.

Machine Learning《机器学习》（月刊）. Machine Learning is an international forum for research on computational approaches to learning. The journal publishes articles reporting substantive results on a wide range of learning methods applied to a variety of learning problems, including but not limited to: Learning Problems: Classification, regression, recognition, and prediction; Problem solving and planning; Reasoning and inference; Data mining; Web mining; Scientific discovery; Information retrieval; Natural language processing; Design and diagnosis; Vision and speech perception; Robotics and control; Combinatorial optimization; Game playing; Industrial, financial, and scientific applications of all kinds.

Evolving Systems《进化系统》（双月刊）. Evolving Systems covers surveys, methodological, and application-oriented papers in the area of dynamically evolving systems. ‘Evolving systems’ are inspired by the idea of system model evolution in a dynamically changing and evolving environment. In contrast to the standard approach in machine learning, mathematical modelling and related disciplines where the model structure is assumed and fixed a priori and the problem is focused on parametric optimisation, evolving systems allow the model structure to gradually change/evolve.