An artificial intelligence-based urban road network automatic generation method comprising a data acquisition and input module, a machine learning module, a rule base construction module, a scheme set generation module, and a human-machine interaction display module. Said method also comprises constructing an anchor point distribution module by means of machine learning, distributing anchor points within a planning range having secondary roads as boundaries, generating a road centerline layout scheme set by means of a rectangular dilation method, selecting an acceptable scheme set on the basis of a rule base formed via conversion of urban planning road-related standards, and further automatically generating a road network scheme set, and lastly outputting a scheme to a two-dimensional interactive display device and performing simulation and display. Using machine learning and rules in the field of urban planning to jointly drive achieve of road network design, a simple and effective automatic generation method for an urban road network is provided, which is able to generate multiple schemes within a short period of time, and provides a highly effective and observable reference for artificial intelligence urban planning design implementation.
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