Artificial intelligence (AI) in complex networks has made revolutionary breakthroughs in this century, and AI-driven methods are being increasingly integrated into different scientific research [1–3]. The scientific research of complex networks can be traced back to two aspects. Firstly, the main mathematical subjects of graph theory and statistical physics. One of the major breakthroughs in graph theory is the idea of random graph theory. Complex topologies arise from simple random rules. Random graph theory is often used in conjunction with percolation theory to describe random network modeling. Secondly, complex systems and statistical physics gave birth to a few important theoretical models, such as the Ising model [4–8], mean-field theory, nonequilibrium thermodynamics and dissipative structure theory, synergetic theory, and self-spinning glass model [8, 9]. Moreover, AI plays a crucial role in improving the performance of network dynamics, key node mining, community detection, and recommendation behaviors in complex networks [10, 11]. The social impact of artificial intelligence is becoming increasingly prominent. On the one hand, as the core force of a new round of scientific and technological revolution and industrial reform, artificial intelligence is promoting the upgrading of traditional industries, driving the rapid development of an “unmanned economy”, and having a positive impact on people’s livelihoods, such as intelligent transportation, smart homes, and intelligent medical care. On the other hand, issues such as personal information and privacy protection, intellectual property rights of AI-created content, possible discrimination …