The invention discloses a dynamic capacity prediction method for an overhead transmission line. The dynamic capacity prediction method comprises the following steps of: measuring a real-time wind speed near a conducting wire through a wind speed sensor mounted on an iron tower, measuring a real-time solar radiation temperature and a real-time ambient temperature through a temperature sensor, predicating the wind speeds and solar radiation temperatures within 1 hour, 2 hours and 4 hours in the future through a RBM depth learning mechanism, obtaining a real-time conducting wire temperature through a tensiometer and a tension-temperature fitting curve, substituting the wind speed, a predicted value of solar radiation temperature and the real-time conducting wire temperature into a conducting line capacity calculation model to obtain predicted capacity values of the conducting wire within 1 hour, 2 hours and 4 hours in the future. Compared with the existing neural network and other prediction methods, the prediction accuracy is effectively improved. The problem that the input of the existing neural network prediction method is extremely limited is solved, and the dimension of the input is greatly improved, and a theoretical basis is provided for the analysis of large grid data in the future.