A traffic state prediction method and apparatus, and a device, a medium and a program, which relate to the technical field of intelligent traffic. The method comprises: generating a plurality of chromosome units (101), wherein each chromosome unit is used for representing a type of spatial-temporal convolutional network model; respectively calculating, on the basis of a sample set, a loss value of the spatial-temporal convolutional network model corresponding to each of the plurality of chromosome units (102); according to the loss values of spatial-temporal convolutional network models corresponding to the plurality of chromosome units, updating the plurality of chromosome units, and returning to execute the step of respectively calculating, on the basis of a sample set, a loss value of the spatial-temporal convolutional network model corresponding to each of the plurality of chromosome units until a target chromosome unit that meets a preset condition is determined (103); on the basis of a spatial-temporal convolutional network model corresponding to the target chromosome unit, determining a pre-trained spatial-temporal convolutional network model (104); and on the basis of the pre-trained spatial-temporal convolutional network model, predicting a traffic state (105). By means of the method, the accuracy of predicting a traffic state can be improved.